Human Resources Analysis: Predict Attrition¶

Notebook 1: Exploratory Data Analysis¶

Enterprise Data Science Bootcamp | 2021¶

Post-Graduation in Enterprise Data Science & Analytics¶

Group TP2 C¶

Beatriz Crispim (m20201835)
Inês Resende (m20200844)
João Tiago Homem (m20201791)
Tiago Rodrigues (m20201771)

Table of Contents¶

1. Import and check the dataset
2. Data pre-processing and Understanding
- 2.1. Describe data
- 2.2 Statistical exploration
  - 2.2.1 Skewness
  - 2.2.2 Kurtosis
  - 2.2.3 Correlation Matrix
  - 2.2.4 Binning
- 2.3 Visual exploration
  - 2.3.1 Attrition demographics
    - 2.3.1.1 Attrition
    - 2.3.1.2 Gender
    - 2.3.1.3 Age & Distance from home
    - 2.3.1.4 Marital Status and Education Field
    - 2.3.1.5 Business Travel
  - 2.3.2 Job demographics
    - 2.3.2.1 Job demographics Years with current manager, years in current role, years at the company and years since last promotion
    - 2.3.2.2 Years at company
    - 2.3.2.3 Department, Job Role , Business Travel, OverTime , Job Level
    - 2.3.2.4 Job Role
    - 2.3.2.5 OverTime
    - 2.3.2.6 Job Level
    - 2.3.2.7 Job Level vs Years at company
    - 2.3.2.8 Job Role vs Business Travel
    - 2.3.2.9 Job Role vs Job Level
  - 2.3.3 Employee Satisfaction
    - 2.3.3.1 Work Life Balance, job involvement
    - 2.3.3.2 Environment Satisfaction, Job Satisfaction, Relationship Satisfaction, Job Involvement, Work Life Balance
    - 2.3.3.3 Job Role vs Work life Balance
    - 2.3.3.4 Job Satisfaction vs Years at company
    - 2.3.3.5 Job Role vs Job Satisfaction
  - 2.3.4 Income and hours of work
    - 2.3.4.1 Job Role vs Montly Income
    - 2.3.4.2 Age vs Montly Income
  - 2.3.5 Performance and training relation
  - 2.3.6 Training Times Last Year
  - 2.3.7 Performance and last promotion/SalarayHike relation
    - 2.3.7.1 Age vs Years since last promotion
  - 2.3.8 Stock option level
    - 2.3.8.1 Age vs Stock option level
  - 2.3.9 Years at company
    - 2.3.9.1 Years at company vs Total working years
    - 2.3.9.2 Years at company vs Year since last promotion
3. Data Transformation
- 3.1 Transform categorical variables
4. Segmentation
- 4.1 Segmentação attrition='No'
  - 4.1.1 Job Role
  - 4.1.2 Stock option level
  - 4.1.4 Job involvement
  - 4.1.5 Business Travel
  - 4.1.6 OverTime
  - 4.1.7 Training times last year
- 4.2 Segmentation by job Role
  - 4.2.1 Sales representative (considering attrition)
    - 4.2.1.1 Gender
    - 4.2.1.2 Overtime
    - 4.2.1.3 Montly Income
    - 4.2.1.4 Job Level
    - 4.2.1.5 Stock option level
    - 4.2.1.6 Satisfaction
    - 4.2.1.7 Business Travel
    - 4.2.1.8 Years in current Role
  - 4.2.2 Laboratory Technician (considering attrition)
    - 4.2.2.1 Montly Income
    - 4.2.2.2 Years in current role
    - 4.2.2.3 Years at company
    - 4.2.2.4 Stock option level
    - 4.2.2.5 Job Level
    - 4.2.2.6 Over Time
    - 4.2.2.7 Work Life balance
    - 4.2.2.8 Satisfaction
  - 4.2.3 Human resources
    - 4.2.3.1 Montly Income
    - 4.2.3.2 Over time
    - 4.2.3.3 Training times last year
    - 4.2.3.4 Stock option level
    - 4.2.3.5 Job Level
    - 4.2.3.6 Distance from home
    - 4.2.3.7 Satisfaction
    - 4.2.3.8 Percent Salary hike

Import libraries¶

import pandas as pd
import numpy as np
import scipy as sp
import seaborn as sns
import matplotlib.pyplot as plt
import statsmodels as sm
import os
import shutil
from zipfile import ZipFile
import warnings
warnings.filterwarnings('ignore')
from pylab import cm
import matplotlib.font_manager
from matplotlib.ticker import (MultipleLocator, AutoMinorLocator)
from scipy import stats
import matplotlib.patches as mpatches
from sklearn.preprocessing import MinMaxScaler
from sklearn.cluster import KMeans
import math

Load style¶

plt.rcParams['font.size'] = 12
plt.rcParams['mathtext.fontset'] = 'custom'
plt.rcParams['mathtext.rm'] = 'Bitstream Vera Sans'
plt.rcParams['mathtext.it'] = 'Bitstream Vera Sans:italic'
plt.rcParams['mathtext.bf'] = 'Bitstream Vera Sans:bold'
plt.rcParams["text.usetex"] = False
plt.rcParams['axes.linewidth'] = 1
plt.rcParams['xtick.direction'] = 'inout'
plt.rcParams['ytick.direction'] = 'inout'
plt.rcParams['xtick.major.size'] = 5.0
plt.rcParams['xtick.minor.size'] = 3.0
plt.rcParams['ytick.major.size'] = 5.0
plt.rcParams['ytick.minor.size'] = 3.0
plt.rcParams['figure.dpi'] = 100
plt.rcParams['lines.markersize'] = 5
plt.rcParams['lines.linewidth'] = 0.7
plt.rcParams['lines.linestyle'] = '--'
plt.rcParams['lines.marker'] = '.'
plt.rcParams['figure.figsize'] = 8,6

#Auxiliar function
def stats (lista):
    
    mean = '{:.2f}'.format(lista.mean())
    Q1 = '{:.2f}'.format(np.percentile(lista, 25))
    Q3 = '{:.2f}'.format(np.percentile(lista, 75))
    skew = '{:.2f}'.format(sp.stats.skew(lista) )
    return (mean, Q1, Q3, skew)

#Auxiliar function
def statss (lista):
    
    mean = '{:.2f}'.format(lista.median())
    Q1 = '{:.2f}'.format(np.percentile(lista, 25))
    Q3 = '{:.2f}'.format(np.percentile(lista, 75))
    skew = '{:.2f}'.format(sp.stats.skew(lista) )
    return (mean, Q1, Q3, skew)

def plots1 (var):
    
    collumn=df[var]
    maxim=collumn.max()
    minim=collumn.min()

    lista = df[df['Attrition'] == 'Yes']    

    fig, axes = plt.subplots(2,2, figsize = (5,6),gridspec_kw={'height_ratios': [1, 4]})
    g1=sns.histplot(ax = axes[1,0], data = lista ,x = var, color="coral", bins = 30)
    g2=sns.boxplot(ax = axes[0,0], data = lista, x = var, color="orange" )
    g2.set(xlabel=None)
    g2.set(title='Attrition = Yes')
    res = stats (lista[var])

    axes[1,0].text(0, -0.15, 'Mean = ' + str(res[0]), transform=axes[1,0].transAxes, fontsize=8,
           color= 'k')
    axes[1,0].text(0.5, -0.15, 'Skewness = ' + str(res[3]), transform=axes[1,0].transAxes, fontsize=8,
           color= 'k')
    axes[1,0].text(0, -0.21, 'Q1 = ' + str(res[1]), transform=axes[1,0].transAxes, fontsize=8,
           color= 'k')
    axes[1,0].text(0.5, -0.21, 'Q3 = ' + str(res[2]), transform=axes[1,0].transAxes, fontsize=8,
           color= 'k')

    plt.xlim(minim,maxim)

    plt.xlabel(var)
    
    lista = df[df['Attrition'] == 'No']    

    g1=sns.histplot(ax = axes[1,1], data = lista ,x = var, color="dodgerblue", bins = 30)
    g2=sns.boxplot(ax = axes[0,1], data = lista, x = var, color="skyblue" )
    g2.set(xlabel=None)
    g2.set(title='Attrition = No')
    res = stats (lista[var])

    axes[1,1].text(0, -0.15, 'Mean = ' + str(res[0]), transform=axes[1,1].transAxes, fontsize=8,
           color= 'k')
    axes[1,1].text(0.5, -0.15, 'Skewness = ' + str(res[3]), transform=axes[1,1].transAxes, fontsize=8,
           color= 'k')
    axes[1,1].text(0, -0.21, 'Q1 = ' + str(res[1]), transform=axes[1,1].transAxes, fontsize=8,
           color= 'k')
    axes[1,1].text(0.5, -0.21, 'Q3 = ' + str(res[2]), transform=axes[1,1].transAxes, fontsize=8,
           color= 'k')
    
    axes[1,1].set_ylabel('')  
    
    plt.xlabel(var)
    plt.xlim(minim,maxim)

    plt.show()

The goals are:

Descriptive Analytics: Find correlations between the different variables, possibility of clustering analysis, build nice visualizations that may help to get better insights for the analysis, check feature’s cardinality and analyze possible highly correlation features that may be removed and still achieve good results.

Predictive Analytics: Build classification models to predict the Attrition probability and the final classification given by the model.

1. Import and check the dataset¶

df = pd.read_csv(r'C:\Users\Tiago\Desktop\Human resources\HR_DS.csv')

print('Rows number before' ,df.shape[0])
df.drop_duplicates(inplace = True)
print('Rows number after removing duplicates' ,df.shape[0])

Rows number before 1470
Rows number after removing duplicates 1470

df.set_index('EmployeeNumber', inplace = True)

2. Data pre-processing and Understanding¶

df.head()

2.1 Describe data¶

df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 1470 entries, 1 to 2068
Data columns (total 34 columns):
 #   Column                    Non-Null Count  Dtype 
---  ------                    --------------  ----- 
 0   Age                       1470 non-null   int64 
 1   Attrition                 1470 non-null   object
 2   BusinessTravel            1470 non-null   object
 3   DailyRate                 1470 non-null   int64 
 4   Department                1470 non-null   object
 5   DistanceFromHome          1470 non-null   int64 
 6   Education                 1470 non-null   int64 
 7   EducationField            1470 non-null   object
 8   EmployeeCount             1470 non-null   int64 
 9   EnvironmentSatisfaction   1470 non-null   int64 
 10  Gender                    1470 non-null   object
 11  HourlyRate                1470 non-null   int64 
 12  JobInvolvement            1470 non-null   int64 
 13  JobLevel                  1470 non-null   int64 
 14  JobRole                   1470 non-null   object
 15  JobSatisfaction           1470 non-null   int64 
 16  MaritalStatus             1470 non-null   object
 17  MonthlyIncome             1470 non-null   int64 
 18  MonthlyRate               1470 non-null   int64 
 19  NumCompaniesWorked        1470 non-null   int64 
 20  Over18                    1470 non-null   object
 21  OverTime                  1470 non-null   object
 22  PercentSalaryHike         1470 non-null   int64 
 23  PerformanceRating         1470 non-null   int64 
 24  RelationshipSatisfaction  1470 non-null   int64 
 25  StandardHours             1470 non-null   int64 
 26  StockOptionLevel          1470 non-null   int64 
 27  TotalWorkingYears         1470 non-null   int64 
 28  TrainingTimesLastYear     1470 non-null   int64 
 29  WorkLifeBalance           1470 non-null   int64 
 30  YearsAtCompany            1470 non-null   int64 
 31  YearsInCurrentRole        1470 non-null   int64 
 32  YearsSinceLastPromotion   1470 non-null   int64 
 33  YearsWithCurrManager      1470 non-null   int64 
dtypes: int64(25), object(9)
memory usage: 402.0+ KB

df.describe(include=[np.number]).T

df.describe(include=[object]).T

2.2 Statistical exploration¶

2.2.1 Skewness¶

sk = df.skew()
sk

Age                         0.413286
DailyRate                  -0.003519
DistanceFromHome            0.958118
Education                  -0.289681
EmployeeCount               0.000000
EnvironmentSatisfaction    -0.321654
HourlyRate                 -0.032311
JobInvolvement             -0.498419
JobLevel                    1.025401
JobSatisfaction            -0.329672
MonthlyIncome               1.369817
MonthlyRate                 0.018578
NumCompaniesWorked          1.026471
PercentSalaryHike           0.821128
PerformanceRating           1.921883
RelationshipSatisfaction   -0.302828
StandardHours               0.000000
StockOptionLevel            0.968980
TotalWorkingYears           1.117172
TrainingTimesLastYear       0.553124
WorkLifeBalance            -0.552480
YearsAtCompany              1.764529
YearsInCurrentRole          0.917363
YearsSinceLastPromotion     1.984290
YearsWithCurrManager        0.833451
dtype: float64

2.2.2 Kurtosis¶

kt = df.kurt()
kt

Age                        -0.404145
DailyRate                  -1.203823
DistanceFromHome           -0.224833
Education                  -0.559115
EmployeeCount               0.000000
EnvironmentSatisfaction    -1.202521
HourlyRate                 -1.196398
JobInvolvement              0.270999
JobLevel                    0.399152
JobSatisfaction            -1.222193
MonthlyIncome               1.005233
MonthlyRate                -1.214956
NumCompaniesWorked          0.010214
PercentSalaryHike          -0.300598
PerformanceRating           1.695939
RelationshipSatisfaction   -1.184814
StandardHours               0.000000
StockOptionLevel            0.364634
TotalWorkingYears           0.918270
TrainingTimesLastYear       0.494993
WorkLifeBalance             0.419460
YearsAtCompany              3.935509
YearsInCurrentRole          0.477421
YearsSinceLastPromotion     3.612673
YearsWithCurrManager        0.171058
dtype: float64

#columns with kurt=0 
df=df.drop(columns=['EmployeeCount', 'StandardHours'])

df['Attrition_aux'] = np.where((df['Attrition']=='Yes'),1,0)

df.head()

2.2.3 Correlation Matrix¶

df_corr = df.corr(method = 'spearman')
figure = plt.figure(figsize=(16,10))
g=sns.heatmap(df_corr, annot=True, fmt = '.1g')

plt.rcParams['font.size'] = 10
plt.rcParams['figure.dpi'] = 80
df_aux = df[['YearsAtCompany', 'YearsInCurrentRole', 'YearsWithCurrManager']].copy()

df_corr = df_aux.corr(method = 'spearman')
figure = plt.figure(figsize=(5,5))
g=sns.heatmap(df_corr, annot=True, fmt = '.1g')

2.2.4 Binning¶

df['age_bins'] = pd.cut(x=df['Age'], bins=[18, 25, 30, 35,40,45,50,55,60])
df['HourlyRate_bins'] = pd.cut(x=df['HourlyRate'], bins=[30, 40, 50, 60,70,80,90,100])

2.3 Visual exploration¶

2.3.1 Attrition demographics¶

2.3.1.1 Attrition¶

plt.rcParams['font.size'] = 11
plt.rcParams['figure.dpi'] = 80
Total=df['Attrition'].value_counts().sum()

values = df['Attrition'].value_counts().keys().tolist()
counts = df['Attrition'].value_counts().tolist()

sns.barplot(x=values,y=counts, palette="deep")

ax = plt.gca()

position = 0
for value in counts:
        ax.text(position, value+10, '{:.1f}%'.format(value / Total * 100) +'  (' + str(value)  +')', color = 'black', ha = 'center', size = 'large' )
        
        position = position + 1

plt.grid(axis='y',color='grey', linestyle='-.', linewidth=0.5)
plt.xlabel('Attrition')
plt.ylabel('Exmployees count')
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
plt.show()

2.3.1.2 Gender¶

var='Gender'
plt.rcParams['font.size'] = 8
plt.rcParams['figure.dpi'] = 110
g = sns.catplot(x="Gender", col="Attrition", col_wrap=4,
                data=df,
                kind="count", height=2.5, aspect=.8, palette='inferno')
ax = g.facet_axis(0,0)
for p in ax.patches:
    ax.text(p.get_x()+0.25, 
            p.get_height() * 1.02, 
           (p.get_height()),   #Used to format it K representation
            color='black', 
            rotation='horizontal', 
            size='medium')
ax = g.facet_axis(0,1)
for p in ax.patches:
    ax.text(p.get_x()+0.25, 
            p.get_height() * 1.02, 
           (p.get_height()),   #Used to format it K representation
            color='black', 
            rotation='horizontal', 
            size='medium')
    
subset_df = df[df['Attrition'] == 'Yes']    

soma = subset_df[var].value_counts().sum()

ax = g.facet_axis(0,0)
for p in ax.patches:
    ax.text(p.get_x()+0.2, 
            p.get_height() /2.5, "{:.3}".format((p.get_height()/soma)*100) + " %", 
            #Used to format it K representation
            color='w', 
            rotation='horizontal', 
            size='medium')    
    
subset_df = df[df['Attrition'] == 'No']    

soma = subset_df[var].value_counts().sum()
    
ax = g.facet_axis(0,1)
for p in ax.patches:
    ax.text(p.get_x()+0.1, 
            p.get_height() /2.5, "{:.4}".format((p.get_height()/soma)*100) + " %", 
            #Used to format it K representation
            color='w', 
            rotation='horizontal', 
            size='medium')

variaveis = ['Gender'] 

Total=df['Department'].value_counts().sum()
variaveis_len = len(variaveis)

fig, axes = plt.subplots( nrows=1, ncols=1, figsize=(6, 5), dpi=100)
for i,var in enumerate(variaveis):

    subset_df = df[df['Attrition'] == 'Yes']    

    subset_counts = subset_df[var].value_counts().tolist()
    subset_values = subset_df[var].value_counts().keys().tolist()
    
    total_counts=[]
    
    for x in subset_values:
        total_counts.append((df[var].values == x).sum())
        
    for k,j in enumerate(subset_counts):
        subset_counts[k]= subset_counts[k]/total_counts[k]*100 
        subset_counts[k]=round(subset_counts[k],1)
        
    ax=sns.barplot(x=subset_values, y=subset_counts, color='steelblue', data=subset_df,edgecolor='k'      )
    
    for p in ax.patches:
        txt = str(p.get_height().round(2)) 
        txt_x = p.get_x() 
        txt_y = p.get_height()
        plt.text(txt_x+p.get_width()/ 2.0,txt_y+0.05,txt, ha='center', va='bottom',fontsize='13')

        rects = ax.patches
      
    plt.ylabel('Percentage of employees in attrition ')
    plt.title(var)
    
plt.tight_layout()
plt.show()

2.3.1.3 Age & Distance from home¶

variaveis = ['Age','DistanceFromHome'] 

fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(12, 6), dpi=100)

for i,var in enumerate(variaveis):
           
    sns.histplot(df[df['Attrition'] == 'No'][var],ax = axes[i], bins = 20,color='darkgreen').set_title(str(var)+ ' (Attrition= No)')
    
    res = stats (df[df['Attrition'] == 'No'][var])   
    
    axes[i].text(0.1+i*1.25, -0.15, 'Mean = ' + str(res[0]), transform=axes[0].transAxes, fontsize=12,
           color= 'k')
    axes[i].text(0.1+i*1.25, -0.2, 'Skewness = ' + str(res[3]), transform=axes[0].transAxes, fontsize=12,
           color= 'k')
    axes[i].text(0.1+i*1.25, -0.25, 'Q1 = ' + str(res[1]), transform=axes[0].transAxes, fontsize=12,
           color= 'k')
    axes[i].text(0.1+i*1.25, -0.3, 'Q3 = ' + str(res[2]), transform=axes[0].transAxes, fontsize=12,
           color= 'k')          
     
for i in range (1,2):
    axes[i].set_ylabel('')  
    axes[i].set_xlabel('')  
for i in range (0,2):   
    axes[i].grid(color='grey', linestyle='-.', linewidth=0.5)    

plt.tight_layout()

plt.show()

fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(12, 6), dpi=300)

for i,var in enumerate(variaveis):
            
    sns.histplot(df[df['Attrition'] == 'Yes'][var],ax = axes[i], bins = 20,color='darkred').set_title(str(var)+ ' (Attrition= Yes)')
    
    res = stats (df[df['Attrition'] == 'Yes'][var])   
    
    axes[i].text(0.1+i*1.25, -0.15, 'Mean = ' + str(res[0]), transform=axes[0].transAxes, fontsize=12,
           color= 'k')
    axes[i].text(0.1+i*1.25, -0.2, 'Skewness = ' + str(res[3]), transform=axes[0].transAxes, fontsize=12,
           color= 'k')
    axes[i].text(0.1+i*1.25, -0.25, 'Q1 = ' + str(res[1]), transform=axes[0].transAxes, fontsize=12,
           color= 'k')
    axes[i].text(0.1+i*1.25, -0.3, 'Q3 = ' + str(res[2]), transform=axes[0].transAxes, fontsize=12,
           color= 'k')      
    for i in range (0,2):   
        axes[i].grid(color='grey', linestyle='-.', linewidth=0.7)    

for i in range (1,2):
    axes[i].set_ylabel('')  
    axes[i].set_xlabel('')  

plt.tight_layout()

plt.show()

plt.rcParams['font.size'] = 8
plt.rcParams['figure.dpi'] = 100
plots1('Age')

2.3.1.4 Marital Status and Education Field¶

plt.rcParams['font.size'] = 10
variaveis = ['EducationField','MaritalStatus'] 

Total=df['Department'].value_counts().sum()
variaveis_len = len(variaveis)

fig, axes = plt.subplots( nrows=1, ncols=variaveis_len, figsize=(12, 3), dpi=100)
for i,var in enumerate(variaveis):

    df_aux = (df.groupby(['Attrition'])[var]
                         .value_counts(normalize=True)
                         .rename('percentage')
                         .mul(100)
                         .reset_index()
                         )
    ax = sns.barplot(x=var, y="percentage", hue="Attrition", palette='Set2', data=df_aux,ax = axes[i])
    ax.set_ylim(0,60)
    ax.set_ylabel('Percentage of employees (%)')
    ax.set_xlabel('')
    _ = plt.setp(ax.get_xticklabels(), rotation=45) 

    for p in axes[i].patches:
        txt = str(p.get_height().round(2)) 
        txt_x = p.get_x() 
        txt_y = p.get_height()
        axes[i].text(txt_x+p.get_width()/ 2.0,txt_y+0.3,txt, ha='center', va='bottom')

        rects = ax.patches

variaveis = ['EducationField','MaritalStatus'] 

Total=df['Department'].value_counts().sum()
variaveis_len = len(variaveis)

fig, axes = plt.subplots( nrows=1, ncols=variaveis_len, figsize=(12, 3), dpi=300)
for i,var in enumerate(variaveis):

    subset_df = df[df['Attrition'] == 'Yes']    

    subset_counts = subset_df[var].value_counts().tolist()
    subset_values = subset_df[var].value_counts().keys().tolist()
    
    total_counts=[]
    
    for x in subset_values:
        total_counts.append((df[var].values == x).sum())
        
    for k,j in enumerate(subset_counts):
        subset_counts[k]= subset_counts[k]/total_counts[k]*100 
        subset_counts[k]=round(subset_counts[k],1)
        
    ax = sns.barplot(y=subset_values, x=subset_counts, color='steelblue', data=subset_df,edgecolor='k',
                     ax = axes[i])
    
    for i, v in enumerate(subset_counts):
    
        ax.text(v, i+0.1, str(v) , color='k', fontweight='bold',fontsize=12,ha='right')
      
    ax.set_xlabel('Percentage of employees in attrition ')
    ax.set_title(var)
    
    _ = plt.setp(ax.get_xticklabels(), rotation=45) 
    
plt.tight_layout()
plt.show()

plt.rcParams['font.size'] = 12
plt.rcParams['figure.dpi'] = 100
variaveis = ['MaritalStatus'] 

Total=df['Department'].value_counts().sum()
variaveis_len = len(variaveis)

fig, axes = plt.subplots( nrows=1, ncols=1, figsize=(5, 3), dpi=100)
for i,var in enumerate(variaveis):

    subset_df = df[df['Attrition'] == 'Yes']    

    subset_counts = subset_df[var].value_counts().tolist()
    subset_values = subset_df[var].value_counts().keys().tolist()
    
    total_counts=[]
    
    for x in subset_values:
        total_counts.append((df[var].values == x).sum())
        
    for k,j in enumerate(subset_counts):
        subset_counts[k]= subset_counts[k]/total_counts[k]*100 
        subset_counts[k]=round(subset_counts[k],1)
        
    ax = sns.barplot(y=subset_values, x=subset_counts, color='steelblue', data=subset_df,edgecolor='k')
    patch_h = []    
    for patch in ax.patches:
        reading = patch.get_height()
        patch_h.append(reading)
   
    idx_tallest = np.argmax(patch_h)   
    # np.argmax return the index of largest value of the list

    ax.patches[idx_tallest].set_facecolor('lightblue')  
    
    for i, v in enumerate(subset_counts):
    
        ax.text(v, i+0.1, str(v) + " %", color='k', fontweight='bold',fontsize=12,ha='right')
      
    ax.set_xlabel('Percentage of employees in attrition ')
    ax.set_title(var)
    
    _ = plt.setp(ax.get_xticklabels(), rotation=45) 
    
plt.tight_layout()
plt.show()

2.3.1.5 Business Travel¶

plt.rcParams['font.size'] = 12
plt.rcParams['figure.dpi'] = 100
variaveis = ['BusinessTravel'] 

Total=df['Department'].value_counts().sum()
variaveis_len = len(variaveis)

fig, axes = plt.subplots( nrows=1, ncols=1, figsize=(8,4), dpi=80)
for i,var in enumerate(variaveis):

    subset_df = df[df['Attrition'] == 'Yes']    

    subset_counts = subset_df[var].value_counts().tolist()
    subset_values = subset_df[var].value_counts().keys().tolist()
    
    total_counts=[]
    
    for x in subset_values:
        total_counts.append((df[var].values == x).sum())
        
    for k,j in enumerate(subset_counts):
        subset_counts[k]= subset_counts[k]/total_counts[k]*100 
        subset_counts[k]=round(subset_counts[k],1)
    
    df_new = pd.DataFrame({"subset_counts":subset_counts,
                  "subset_values":subset_values})
        
    ax = sns.barplot(y=subset_values, x=subset_counts, color='steelblue', data=df_new,edgecolor='k',
                     order=df_new.sort_values('subset_counts',ascending = False).subset_values)
    patch_h = []    
    for patch in ax.patches:
        reading = patch.get_height()
        patch_h.append(reading)
   
    idx_tallest = np.argmax(patch_h)   
    # np.argmax return the index of largest value of the list

    ax.patches[idx_tallest].set_facecolor('lightblue')  
    values=df_new.sort_values('subset_counts',ascending = False).subset_values
    
    for p in ax.patches:
        txt = str(p.get_width().round(2)) 
        
        txt_x = p.get_width() 
        txt_y = p.get_y()
        
        plt.text(txt_x+2,txt_y+0.5,txt + " %", ha='center', va='bottom',fontsize='13')

        rects = ax.patches
    
    ax.set_xlabel('Percentage of employees in attrition ')
    ax.set_xlim(0,35)
    ax.set_title(var)
    
    _ = plt.setp(ax.get_xticklabels(), rotation=45) 
    
plt.tight_layout()
plt.show()

2.3.2 Job demographics¶

2.3.2.1 Job demographics Years with current manager, years in current role, years at the company and years since last promotion¶

plt.rcParams['font.size'] = 7
variaveis = ['YearsWithCurrManager', 'YearsInCurrentRole', 'YearsAtCompany','YearsSinceLastPromotion'] 

fig, axes = plt.subplots(nrows=1, ncols=4, figsize=(12, 6), dpi=300)

for i,var in enumerate(variaveis):
            
    sns.histplot(df[df['Attrition'] == 'No'][var],ax = axes[i], bins = 20,color='darkgreen').set_title(str(var)+ ' (Attrition= No)')
    
    res = stats (df[df['Attrition'] == 'No'][var])   
    
    axes[i].text(0.1+i*1.25, -0.15, 'Mean = ' + str(res[0]), transform=axes[0].transAxes, fontsize=10,
           color= 'k')
    axes[i].text(0.1+i*1.25, -0.2, 'Skewness = ' + str(res[3]), transform=axes[0].transAxes, fontsize=10,
           color= 'k')
    axes[i].text(0.1+i*1.25, -0.25, 'Q1 = ' + str(res[1]), transform=axes[0].transAxes, fontsize=10,
           color= 'k')
    axes[i].text(0.1+i*1.25, -0.3, 'Q3 = ' + str(res[2]), transform=axes[0].transAxes, fontsize=10,
           color= 'k')   
    axes[i].grid(color='grey', linestyle=':', linewidth=0.5)
    axes[i].spines['top'].set_visible(False)
    axes[i].spines['right'].set_visible(False)
     
for i in range (1,3):
    axes[i].set_ylabel('')  

plt.tight_layout()
plt.show()

fig, axes = plt.subplots(nrows=1, ncols=4, figsize=(12, 6), dpi=300)

for i,var in enumerate(variaveis):
            
    sns.histplot(df[df['Attrition'] == 'Yes'][var],ax = axes[i], bins = 20,color='darkred').set_title(str(var)+ ' (Attrition= Yes)')
    
    res = stats (df[df['Attrition'] == 'Yes'][var])   
    
    axes[i].text(0.1+i*1.25, -0.15, 'Mean = ' + str(res[0]), transform=axes[0].transAxes, fontsize=10,
           color= 'k')
    axes[i].text(0.1+i*1.25, -0.2, 'Skewness = ' + str(res[3]), transform=axes[0].transAxes, fontsize=10,
           color= 'k')
    axes[i].text(0.1+i*1.25, -0.25, 'Q1 = ' + str(res[1]), transform=axes[0].transAxes, fontsize=10,
           color= 'k')
    axes[i].text(0.1+i*1.25, -0.3, 'Q3 = ' + str(res[2]), transform=axes[0].transAxes, fontsize=10,
           color= 'k')     
    axes[i].grid(color='grey', linestyle=':', linewidth=0.5)
    axes[i].spines['top'].set_visible(False)
    axes[i].spines['right'].set_visible(False)
     
for i in range (1,3):
    axes[i].set_ylabel('')  

plt.tight_layout()
plt.show()

2.3.2.2 Years at company¶

plt.rcParams['font.size'] = 8
plt.rcParams['figure.dpi'] = 100
plots1('YearsAtCompany')

2.3.2.3 Department, Job Role , Business Travel, OverTime , Job Level¶

variaveis = ['Department','JobRole','OverTime','BusinessTravel','JobLevel'] 

Total=df['Department'].value_counts().sum()
variaveis_len = len(variaveis)

fig, axes = plt.subplots( nrows=variaveis_len, ncols=1, figsize=(8, 20), dpi=100)
for i,var in enumerate(variaveis):

    df_aux = (df.groupby(['Attrition'])[var]
                         .value_counts(normalize=True)
                         .rename('percentage')
                         .mul(100)
                         .reset_index()
                         )
    ax = sns.barplot(x=var, y="percentage", hue="Attrition", palette='Set1', data=df_aux,ax = axes[i])
#     ax.set_ylim(0,100)
    ax.set_ylabel('Percentage of employees (%)')
    ax.set_xlabel('')
    ax.set_title(var)
    ax.grid(axis='y',color='grey', linestyle='-.', linewidth=0.5)
    _ = plt.setp(ax.get_xticklabels(), rotation=20) 

    for p in axes[i].patches:
        txt = str(p.get_height().round(2)) 
        txt_x = p.get_x() 
        txt_y = p.get_height()
        axes[i].text(txt_x+p.get_width()/ 2.0,txt_y+0.1,txt, ha='center', va='bottom')

        rects = ax.patches
       
plt.tight_layout()
plt.show()

plt.rcParams['font.size'] = 8
variaveis = ['Department','JobRole','OverTime','BusinessTravel'] 

Total=df['Department'].value_counts().sum()
variaveis_len = len(variaveis)

fig, axes = plt.subplots( nrows=variaveis_len, ncols=1, figsize=(6, 8), dpi=200)
for i,var in enumerate(variaveis):

    subset_df = df[df['Attrition'] == 'Yes']    

    subset_counts = subset_df[var].value_counts().tolist()
    subset_values = subset_df[var].value_counts().keys().tolist()
    
    total_counts=[]
    
    for x in subset_values:
        total_counts.append((df[var].values == x).sum())
        
    for k,j in enumerate(subset_counts):
        subset_counts[k]= subset_counts[k]/total_counts[k]*100 
        subset_counts[k]=round(subset_counts[k],1)
        
    ax = sns.barplot(y=subset_values, x=subset_counts, color='steelblue', data=subset_df,edgecolor='k',
                     ax = axes[i])
    
    for i, v in enumerate(subset_counts):
    
        ax.text(v, i+0.1, str(v) , color='w', fontweight='bold',fontsize=7,ha='right')
      
    ax.set_xlabel('Percentage of employees in attrition ')
    ax.set_title(var)
    
    _ = plt.setp(ax.get_xticklabels(), rotation=45) 
    
plt.tight_layout()
plt.show()

2.3.2.4 Job Role¶

plt.rcParams['font.size'] = 12
plt.rcParams['figure.dpi'] = 100
variaveis = ['JobRole'] 

Total=df['Department'].value_counts().sum()
variaveis_len = len(variaveis)

fig, axes = plt.subplots( nrows=1, ncols=1, figsize=(8,6), dpi=80)
for i,var in enumerate(variaveis):

    subset_df = df[df['Attrition'] == 'Yes']    

    subset_counts = subset_df[var].value_counts().tolist()
    subset_values = subset_df[var].value_counts().keys().tolist()
    
    total_counts=[]
    
    for x in subset_values:
        total_counts.append((df[var].values == x).sum())
        
    for k,j in enumerate(subset_counts):
        subset_counts[k]= subset_counts[k]/total_counts[k]*100 
        subset_counts[k]=round(subset_counts[k],1)
    
    df_new = pd.DataFrame({"subset_counts":subset_counts,
                  "subset_values":subset_values})
        
    ax = sns.barplot(y=subset_values, x=subset_counts, color='steelblue', data=df_new,edgecolor='k',
                     order=df_new.sort_values('subset_counts',ascending = False).subset_values)
    patch_h = []    
    for patch in ax.patches:
        reading = patch.get_height()
        patch_h.append(reading)
   
    idx_tallest = np.argmax(patch_h)   
    # np.argmax return the index of largest value of the list

    ax.patches[idx_tallest].set_facecolor('lightblue')  
    values=df_new.sort_values('subset_counts',ascending = False).subset_values
    
    for p in ax.patches:
        txt = str(p.get_width().round(2)) 
        
        txt_x = p.get_width() 
        txt_y = p.get_y()
        
        plt.text(txt_x+3.5,txt_y+0.6,txt + " %", ha='center', va='bottom',fontsize='13')

        rects = ax.patches
    
    ax.set_xlabel('Percentage of employees in attrition ')
    ax.set_xlim(0,50)
    ax.set_title(var)
    
    _ = plt.setp(ax.get_xticklabels(), rotation=45) 
    
plt.tight_layout()
plt.show()

2.3.2.5 OverTime¶

plt.rcParams['font.size'] = 14
plt.rcParams['figure.dpi'] = 100
variaveis = ['OverTime'] 

Total=df['Department'].value_counts().sum()
variaveis_len = len(variaveis)

fig, axes = plt.subplots( nrows=1, ncols=1, figsize=(4,3), dpi=80)
for i,var in enumerate(variaveis):

    subset_df = df[df['Attrition'] == 'Yes']    

    subset_counts = subset_df[var].value_counts().tolist()
    subset_values = subset_df[var].value_counts().keys().tolist()
    
    total_counts=[]
    
    for x in subset_values:
        total_counts.append((df[var].values == x).sum())
        
    for k,j in enumerate(subset_counts):
        subset_counts[k]= subset_counts[k]/total_counts[k]*100 
        subset_counts[k]=round(subset_counts[k],1)
    
    df_new = pd.DataFrame({"subset_counts":subset_counts,
                  "subset_values":subset_values})
        
    ax = sns.barplot(y=subset_values, x=subset_counts, color='steelblue', data=df_new,edgecolor='k',
                     order=df_new.sort_values('subset_counts',ascending = False).subset_values)
    patch_h = []    
    for patch in ax.patches:
        reading = patch.get_height()
        patch_h.append(reading)
   
    idx_tallest = np.argmax(patch_h)   
    # np.argmax return the index of largest value of the list

    ax.patches[idx_tallest].set_facecolor('lightblue')  
    values=df_new.sort_values('subset_counts',ascending = False).subset_values
    
    
    for p in ax.patches:
        txt = str(p.get_width().round(2)) 
        
        txt_x = p.get_width() 
        txt_y = p.get_y()
        
        plt.text(txt_x+7
                 ,txt_y+0.55,txt + " %", ha='center', va='bottom',fontsize='13')

        rects = ax.patches
    
    ax.set_xlabel('Percentage of employees in attrition ')
    ax.set_xlim(0,50)
    ax.set_title(var)
    
    _ = plt.setp(ax.get_xticklabels(), rotation=45) 
    
plt.tight_layout()
plt.show()

2.3.2.6 Job Level¶

variaveis = ['JobLevel'] 

Total=df['Department'].value_counts().sum()
variaveis_len = len(variaveis)

fig, axes = plt.subplots( nrows=1, ncols=1, figsize=(6, 5), dpi=100)
for i,var in enumerate(variaveis):

    subset_df = df[df['Attrition'] == 'Yes']    

    subset_counts = subset_df[var].value_counts().tolist()
    subset_values = subset_df[var].value_counts().keys().tolist()
    
    total_counts=[]
    
    for x in subset_values:
        total_counts.append((df[var].values == x).sum())
        
    for k,j in enumerate(subset_counts):
        subset_counts[k]= subset_counts[k]/total_counts[k]*100 
        subset_counts[k]=round(subset_counts[k],1)
    
    df_new = pd.DataFrame({"subset_counts":subset_counts,
                  "subset_values":subset_values})
        
    ax=sns.barplot(x=subset_values, y=subset_counts, color='steelblue', data=subset_df,edgecolor='k'      )
    
    for patch in ax.patches:
        reading = patch.get_height()
        patch_h.append(reading)
   
    idx_tallest = np.argmax(patch_h)   
    # np.argmax return the index of largest value of the list

    ax.patches[idx_tallest].set_facecolor('lightblue')  
    
    for p in ax.patches:
        txt = str(p.get_height().round(2)) 
        txt_x = p.get_x() 
        txt_y = p.get_height()
        plt.text(txt_x+p.get_width()/ 2.0,txt_y+0.02,txt + ' %', ha='center', va='bottom',fontsize='13')

        rects = ax.patches
      
    plt.ylabel('Percentage of employees in attrition ')
    plt.title(var)
    
plt.tight_layout()
plt.show()

2.3.2.7 Job Level and Years at company¶

plt.figure(figsize=(7,5),dpi=100)
box_plot = sns.boxplot(x="JobLevel", y="YearsAtCompany", data=df,palette="crest",hue='Attrition')

medians = df.groupby(['JobLevel'])['YearsAtCompany'].median()
vertical_offset =df['YearsAtCompany'].median() * 0.1 

# for xtick in box_plot.get_xticks():
#     box_plot.text(xtick,medians[xtick] + vertical_offset,medians[xtick], 
#             horizontalalignment='center',size='medium',color='w',weight='semibold')
plt.xlabel("JobLevel")

plt.ylabel("YearsAtCompany")
plt.show()

2.3.2.8 Job Role and Business Travel¶

var1='JobRole'
var2='BusinessTravel'

plt.rcParams['font.size'] = 10

df_aux_YES =  df.loc[df['Attrition'] == 'Yes'] 

df_var1_vs_var2= pd.crosstab(df_aux_YES.JobRole, df_aux_YES.BusinessTravel ,normalize='index')*100

fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(7, 5), dpi=100)
sns.heatmap(df_var1_vs_var2, linewidths=.5, annot=True, cmap="YlGnBu" , fmt='.1f',annot_kws={"size": 8},ax = axes[0] )
axes[0] .set_title('Attrition = YES' )
axes[0].set_xlabel(var2, fontsize = 10) 
axes[0].set_ylabel(var1, fontsize = 10) 

df_aux_No =  df.loc[df['Attrition'] == 'No'] 

df_var1_vs_var3= pd.crosstab(df_aux_No.JobRole, df_aux_No.BusinessTravel,normalize='index')*100

sns.heatmap(df_var1_vs_var3, linewidths=.5, annot=True, cmap="YlGnBu"  ,fmt='.1f', annot_kws={"size": 8},ax = axes[1] )
axes[1] .set_title('Attrition = No' )
axes[1].set_xlabel(var2, fontsize = 10) 
axes[1].set_ylabel('') 

plt.tight_layout()
plt.show()

2.3.2.9 Job Role vs Job Level¶

var1='JobRole'
var2='JobLevel'

df_aux_YES =  df.loc[df['Attrition'] == 'Yes'] 

df_var1_vs_var2= pd.crosstab(df_aux_YES.JobRole, df_aux_YES.JobLevel ,normalize='index')*100

fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(8, 4), dpi=100)
sns.heatmap(df_var1_vs_var2, linewidths=.5, annot=True, cmap="YlGnBu" , fmt='.1f',annot_kws={"size": 8},ax = axes[0] )
axes[0] .set_title('Attrition = YES' )
axes[0].set_xlabel(var2, fontsize = 10) 
axes[0].set_ylabel(var1, fontsize = 10) 

df_aux_No =  df.loc[df['Attrition'] == 'No'] 

df_var1_vs_var3= pd.crosstab(df_aux_No.JobRole, df_aux_No.JobLevel,normalize='index')*100

sns.heatmap(df_var1_vs_var3, linewidths=.5, annot=True, cmap="YlGnBu"  ,fmt='.1f', annot_kws={"size": 8},ax = axes[1] )
axes[1] .set_title('Attrition = No' )
axes[1].set_xlabel(var2, fontsize = 10) 
axes[1].set_ylabel('') 

plt.tight_layout()
plt.show()

2.3.3 Employee Satisfaction¶

2.3.3.1 Work Life Balance, job involvement¶

variaveis = ['JobInvolvement','WorkLifeBalance'] 

Total=df['Department'].value_counts().sum()
variaveis_len = len(variaveis)

fig, axes = plt.subplots( nrows=variaveis_len, ncols=1, figsize=(4, 6), dpi=120)
for i,var in enumerate(variaveis):

    df_aux = (df.groupby(['Attrition'])[var]
                         .value_counts(normalize=True)
                         .rename('percentage')
                         .mul(100)
                         .reset_index()
                         )
    ax = sns.barplot(x=var, y="percentage", hue="Attrition", palette='Set2', data=df_aux,ax = axes[i])
#     ax.set_ylim(0,100)
    ax.set_ylabel('Percentage of employees (%)')
    ax.set_xlabel('')
    ax.set_title(var)
    ax.grid(axis='y',color='grey', linestyle='-.', linewidth=0.5)
    _ = plt.setp(ax.get_xticklabels(), rotation=0) 

    for p in axes[i].patches:
        txt = str(p.get_height().round(2)) 
        txt_x = p.get_x() 
        txt_y = p.get_height()
        axes[i].text(txt_x+p.get_width()/ 2.0,txt_y+0.1,txt, ha='center', va='bottom')

        rects = ax.patches
        sns.despine(ax=axes[i], offset=5)

plt.tight_layout()
plt.show()

2.3.3.2 Environment Satisfaction, Job Satisfaction, Relationship Satisfaction, Job Involvement, Work Life Balance¶

variaveis = ['EnvironmentSatisfaction', 'JobSatisfaction', 'RelationshipSatisfaction','JobInvolvement','WorkLifeBalance'] 

plt.rcParams['font.size'] = 8
Total=df['Department'].value_counts().sum()
variaveis_len = len(variaveis)

fig, axes = plt.subplots( nrows=variaveis_len, ncols=1, figsize=(5, 13), dpi=120)
for i,var in enumerate(variaveis):

    subset_df = df[df['Attrition'] == 'Yes']    

    subset_counts = subset_df[var].value_counts().tolist()
    subset_values = subset_df[var].value_counts().keys().tolist()
    
    total_counts=[]
    
    for x in subset_values:
        total_counts.append((df[var].values == x).sum())
        
    for k,j in enumerate(subset_counts):
        subset_counts[k]= subset_counts[k]/total_counts[k]*100 
        subset_counts[k]=round(subset_counts[k],1)
        
    ax=sns.barplot(x=subset_values, y=subset_counts, color='green', data=subset_df,edgecolor='k' ,ax=axes[i]     )
    
    for p in axes[i].patches:
        txt = str(p.get_height().round(2)) 
        txt_x = p.get_x() 
        txt_y = p.get_height()
        axes[i].text(txt_x+p.get_width()/ 2.0,txt_y-0.3,txt, ha='center', va='top',fontsize=10,color='w')

        rects = ax.patches

    ax.set_ylabel('Percentage of employees in attrition ')
    ax.set_title(var)
    
plt.tight_layout()
plt.show()

2.3.3.3 Job Role vs Work life Balance¶

var1='JobLevel'
var2='WorkLifeBalance'
plt.rcParams['font.size'] = 10

df_aux_YES =  df.loc[df['Attrition'] == 'Yes'] 

df_var1_vs_var2= pd.crosstab(df_aux_YES.JobLevel, df_aux_YES.WorkLifeBalance ,normalize='index')*100

fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(6, 3), dpi=120)
sns.heatmap(df_var1_vs_var2, linewidths=.5, annot=True, cmap="YlGnBu" , fmt='.1f',annot_kws={"size": 8},ax = axes[0] )
axes[0] .set_title('Attrition = YES' )
axes[0].set_xlabel(var2, fontsize = 10) 
axes[0].set_ylabel(var1, fontsize = 10) 

df_aux_No =  df.loc[df['Attrition'] == 'No'] 

df_var1_vs_var3= pd.crosstab(df_aux_No.JobLevel, df_aux_No.WorkLifeBalance,normalize='index')*100

sns.heatmap(df_var1_vs_var3, linewidths=.5, annot=True, cmap="YlGnBu"  ,fmt='.1f', annot_kws={"size": 8},ax = axes[1] )
axes[1] .set_title('Attrition = No' )
axes[1].set_xlabel(var2, fontsize = 10) 
axes[1].set_ylabel('') 

plt.tight_layout()
plt.show()

2.3.3.4 Job Satisfaction vs Years at company¶

plt.figure(figsize=(6,5),dpi=100)
box_plot = sns.boxplot(x="JobSatisfaction", y="YearsAtCompany", data=df,palette="crest",hue='Attrition')

medians = df.groupby(['JobSatisfaction'])['YearsAtCompany'].median()
vertical_offset =df['YearsAtCompany'].median() * 0.1 

# for xtick in box_plot.get_xticks():
#     box_plot.text(xtick,medians[xtick] + vertical_offset,medians[xtick], 
#             horizontalalignment='center',size='medium',color='w',weight='semibold')
plt.xlabel("JobSatisfaction")

plt.ylabel("YearsAtCompany")
plt.show()

2.3.3.5 Job Role vs Job Satisfaction¶

var1='JobRole'
var2='JobSatisfaction'

df_aux_YES =  df.loc[df['Attrition'] == 'Yes'] 

df_var1_vs_var2= pd.crosstab(df_aux_YES.JobRole, df_aux_YES.JobSatisfaction ,normalize='index')*100

fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(8, 4), dpi=150)
sns.heatmap(df_var1_vs_var2, linewidths=.5, annot=True, cmap="YlGnBu" , fmt='.1f',annot_kws={"size": 8},ax = axes[0] )
axes[0] .set_title('Attrition = YES' )
axes[0].set_xlabel(var2, fontsize = 10) 
axes[0].set_ylabel(var1, fontsize = 10) 

df_aux_No =  df.loc[df['Attrition'] == 'No'] 

df_var1_vs_var3= pd.crosstab(df_aux_No.JobRole, df_aux_No.JobSatisfaction,normalize='index')*100

sns.heatmap(df_var1_vs_var3, linewidths=.5, annot=True, cmap="YlGnBu"  ,fmt='.1f', annot_kws={"size": 8},ax = axes[1] )
axes[1] .set_title('Attrition = No' )
axes[1].set_xlabel(var2, fontsize = 10) 
axes[1].set_ylabel('') 

plt.tight_layout()
plt.show()

2.3.4 Income and hours of work¶

plt.rcParams['font.size'] = 14
plt.rcParams['figure.dpi'] = 100
variaveis = ['MonthlyIncome','PercentSalaryHike'] 

fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(12, 6))

for i,var in enumerate(variaveis):
           
    sns.histplot(df[df['Attrition'] == 'No'][var],ax = axes[i], bins = 20,color='darkgreen').set_title(str(var)+ ' (Attrition= No)')
    
    res = stats (df[df['Attrition'] == 'No'][var])   
    
    axes[i].text(0.1+i*1.25, -0.15, 'Mean = ' + str(res[0]), transform=axes[0].transAxes, fontsize=13,
           color= 'k')
    axes[i].text(0.1+i*1.25, -0.2, 'Skewness = ' + str(res[3]), transform=axes[0].transAxes, fontsize=13,
           color= 'k')
    axes[i].text(0.1+i*1.25, -0.25, 'Q1 = ' + str(res[1]), transform=axes[0].transAxes, fontsize=13,
           color= 'k')
    axes[i].text(0.1+i*1.25, -0.3, 'Q3 = ' + str(res[2]), transform=axes[0].transAxes, fontsize=13,
           color= 'k')          
     
for i in range (1,2):
    axes[i].set_ylabel('')
    
for i in range (0,2):   
    axes[i].grid(color='grey', linestyle='-.', linewidth=0.5)    
    sns.despine(ax=axes[i], offset=5)
    axes[i].set_xlabel('')

plt.tight_layout()

plt.show()

fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(12, 6), dpi=300)

for i,var in enumerate(variaveis):
            
    sns.histplot(df[df['Attrition'] == 'Yes'][var],ax = axes[i], bins = 20,color='darkred').set_title(str(var)+ ' (Attrition= Yes)')
    
    res = stats (df[df['Attrition'] == 'Yes'][var])   
    
    axes[i].text(0.1+i*1.25, -0.15, 'Mean = ' + str(res[0]), transform=axes[0].transAxes, fontsize=13,
           color= 'k')
    axes[i].text(0.1+i*1.25, -0.2, 'Skewness = ' + str(res[3]), transform=axes[0].transAxes, fontsize=13,
           color= 'k')
    axes[i].text(0.1+i*1.25, -0.25, 'Q1 = ' + str(res[1]), transform=axes[0].transAxes, fontsize=13,
           color= 'k')
    axes[i].text(0.1+i*1.25, -0.3, 'Q3 = ' + str(res[2]), transform=axes[0].transAxes, fontsize=13,
           color= 'k')      
    for i in range (0,2):   
        axes[i].grid(color='grey', linestyle='-.', linewidth=0.7)  
        axes[i].set_xlabel('')
        
        sns.despine(ax=axes[i], offset=5)
     
for i in range (1,2):
    axes[i].set_ylabel('')  
    
plt.tight_layout()

plt.show()

2.3.4.1 Job Role vs Montly Income¶

fig, ax = plt.subplots( nrows=1, ncols=1, figsize=(8, 5), dpi=100)
ax = sns.boxplot(x="JobRole", y="MonthlyIncome", data=df,palette="GnBu",hue='Attrition')

medians = df.groupby(['JobRole'])['MonthlyIncome'].median()
vertical_offset =df['MonthlyIncome'].median() * 0.1 
ax.grid(axis='y',color='grey', linestyle='-.', linewidth=0.5)
_ = plt.setp(ax.get_xticklabels(), rotation=20,ha='right') 

plt.xlabel("JobRole")

plt.ylabel("MonthlyIncome")

plt.show()

2.3.4.2 Age vs Montly Income¶

plt.rcParams['font.size'] = 12
plt.rcParams['figure.dpi'] = 100
plt.figure(figsize=(10,6))
box_plot = sns.boxplot(x="age_bins", y="MonthlyIncome", data=df,palette="crest",hue='Attrition')

medians = df.groupby(['age_bins'])['MonthlyIncome'].median()
vertical_offset =df['MonthlyIncome'].median() * 0.1 

# for xtick in box_plot.get_xticks():
#     box_plot.text(xtick,medians[xtick] + vertical_offset,medians[xtick], 
#             horizontalalignment='center',size='medium',color='w',weight='semibold')
plt.xlabel("Age group")
plt.title ("Montly Income per age group")
plt.ylabel("Montly Income")
plt.show()

2.3.5 Performance and training relation¶

variaveis = ['PerformanceRating', 'TrainingTimesLastYear'] 

Total=df['Department'].value_counts().sum()
variaveis_len = len(variaveis)

fig, axes = plt.subplots( nrows=1, ncols=2, figsize=(10, 5), dpi=100)
for i,var in enumerate(variaveis):

    subset_df = df[df['Attrition'] == 'Yes']    

    subset_counts = subset_df[var].value_counts().tolist()
    subset_values = subset_df[var].value_counts().keys().tolist()
    
    total_counts=[]
    
    for x in subset_values:
        total_counts.append((df[var].values == x).sum())
        
    for k,j in enumerate(subset_counts):
        subset_counts[k]= subset_counts[k]/total_counts[k]*100 
        subset_counts[k]=round(subset_counts[k],1)
        
    ax=sns.barplot(x=subset_values, y=subset_counts, color='green', data=subset_df,edgecolor='k' ,ax=axes[i]     )
    
    for p in axes[i].patches:
        txt = str(p.get_height().round(2)) 
        txt_x = p.get_x() 
        txt_y = p.get_height()
        axes[i].text(txt_x+p.get_width()/ 2.0,txt_y-0.3,txt, ha='center', va='top',fontsize=14,color='w')

        rects = ax.patches

    ax.set_ylabel('Percentage of employees in attrition ')
    ax.set_title(var)
    sns.despine(ax=ax, offset=5)

plt.tight_layout()
plt.show()

2.3.6 Training Times Last Year¶

plt.rcParams['font.size'] = 14
plt.rcParams['figure.dpi'] = 100
variaveis = ['TrainingTimesLastYear'] 

Total=df['Department'].value_counts().sum()
variaveis_len = len(variaveis)

fig, axes = plt.subplots( nrows=1, ncols=1, figsize=(8,6), dpi=80)
for i,var in enumerate(variaveis):

    subset_df = df[df['Attrition'] == 'Yes']    

    subset_counts = subset_df[var].value_counts().tolist()
    subset_values = subset_df[var].value_counts().keys().tolist()
    
    total_counts=[]
    
    for x in subset_values:
        total_counts.append((df[var].values == x).sum())
        
    for k,j in enumerate(subset_counts):
        subset_counts[k]= subset_counts[k]/total_counts[k]*100 
        subset_counts[k]=round(subset_counts[k],1)
    
    df_new = pd.DataFrame({"subset_counts":subset_counts,
                  "subset_values":subset_values})
        
    ax = sns.barplot(x=subset_values, y=subset_counts, color='steelblue', data=df_new,edgecolor='k')
    patch_h = []    
    for patch in ax.patches:
        reading = patch.get_height()
        patch_h.append(reading)
   
    idx_tallest = np.argmax(patch_h)   
    # np.argmax return the index of largest value of the list

    ax.patches[idx_tallest].set_facecolor('lightblue')  
    values=df_new.sort_values('subset_counts',ascending = False).subset_values
    
    for p in ax.patches:
        txt = str(p.get_height().round(2)) 
        txt_x = p.get_x() 
        txt_y = p.get_height()
        ax.text(txt_x+p.get_width()/ 2.0,txt_y-0.3,txt +  ' %', ha='center', va='top',fontsize=16,color='k')
    
    ax.set_ylabel('Percentage of employees in attrition ')
    
    ax.set_title(var)
    
    _ = plt.setp(ax.get_xticklabels(), rotation=0) 
    
plt.tight_layout()
plt.show()

2.3.7 Performance and last promotion/SalarayHike relation¶

plt.figure(figsize=(10,6))
box_plot = sns.boxplot(x="PerformanceRating", y="PercentSalaryHike", data=df,palette="Set2",hue='Attrition')

plt.ylabel("PercentSalaryHike")

plt.xlabel("PerformanceRating")
plt.grid(axis='y',color='grey', linestyle='-.', linewidth=0.5)
plt.show()

2.3.7.1 Age vs Years since last promotion¶

plt.figure(figsize=(10,6))
plt.rcParams['font.size'] = 13
plt.rcParams['figure.dpi'] = 100
box_plot = sns.boxplot(y="age_bins", x="YearsSinceLastPromotion", data=df,palette="Set3",hue='Attrition')

plt.ylabel("Age group")
plt.title ("YearsSinceLastPromotion per age group")
plt.xlabel("YearsSinceLastPromotion")
plt.grid(axis='y',color='grey', linestyle='-.', linewidth=0.5)
plt.show()

2.3.8 Stock option level¶

2.3.8.1 Age vs Stock option level¶

variaveis = ['StockOptionLevel','age_bins'] 

Total=df['Department'].value_counts().sum()
variaveis_len = len(variaveis)

fig, axes = plt.subplots( nrows=variaveis_len, ncols=1, figsize=(6, 10), dpi=100)
for i,var in enumerate(variaveis):

    df_aux = (df.groupby(['Attrition'])[var]
                         .value_counts(normalize=True)
                         .rename('percentage')
                         .mul(100)
                         .reset_index()
                         )
    ax = sns.barplot(x=var, y="percentage", hue="Attrition", palette='Set1', data=df_aux,ax = axes[i])
#     ax.set_ylim(0,100)
    ax.set_ylabel('Percentage of employees (%)')
    ax.set_xlabel('')
    ax.set_title(var)
    ax.grid(axis='y',color='grey', linestyle='-.', linewidth=0.5)
    
    for p in axes[i].patches:
        txt = str(p.get_height().round(2)) 
        txt_x = p.get_x() 
        txt_y = p.get_height()
        axes[i].text(txt_x+p.get_width()/ 2.0,txt_y+0.1,txt, ha='center', va='bottom')
        _ = plt.setp(ax.get_xticklabels(), rotation=20,ha='right') 
        
        rects = ax.patches

plt.tight_layout()
plt.show()

plt.rcParams['font.size'] = 14
plt.rcParams['figure.dpi'] = 100
variaveis = ['StockOptionLevel'] 

Total=df['Department'].value_counts().sum()
variaveis_len = len(variaveis)

fig, axes = plt.subplots( nrows=1, ncols=1, figsize=(8,6), dpi=80)
for i,var in enumerate(variaveis):

    subset_df = df[df['Attrition'] == 'Yes']    

    subset_counts = subset_df[var].value_counts().tolist()
    subset_values = subset_df[var].value_counts().keys().tolist()
    
    total_counts=[]
    
    for x in subset_values:
        total_counts.append((df[var].values == x).sum())
        
    for k,j in enumerate(subset_counts):
        subset_counts[k]= subset_counts[k]/total_counts[k]*100 
        subset_counts[k]=round(subset_counts[k],1)
    
    df_new = pd.DataFrame({"subset_counts":subset_counts,
                  "subset_values":subset_values})
        
    ax = sns.barplot(x=subset_values, y=subset_counts, color='steelblue', data=df_new,edgecolor='k')
    patch_h = []    
    for patch in ax.patches:
        reading = patch.get_height()
        patch_h.append(reading)
   
    idx_tallest = np.argmax(patch_h)   
    # np.argmax return the index of largest value of the list

    ax.patches[idx_tallest].set_facecolor('lightblue')  
    values=df_new.sort_values('subset_counts',ascending = False).subset_values
    
    for p in ax.patches:
        txt = str(p.get_height().round(2)) 
        txt_x = p.get_x() 
        txt_y = p.get_height()
        ax.text(txt_x+p.get_width()/ 2.0,txt_y-0.3,txt +  ' %', ha='center', va='top',fontsize=16,color='k')
    
    ax.set_ylabel('Percentage of employees in attrition ')
    
    ax.set_title(var)
    
    _ = plt.setp(ax.get_xticklabels(), rotation=0) 
    
plt.tight_layout()
plt.show()

2.3.9 Years at company¶

plt.rcParams['font.size'] = 12
plt.rcParams['figure.dpi'] = 100
variaveis = ['YearsAtCompany'] 

Total=df['Department'].value_counts().sum()
variaveis_len = len(variaveis)

fig, axes = plt.subplots( nrows=1, ncols=1, figsize=(8, 6), dpi=100)
for i,var in enumerate(variaveis):

    subset_df = df[df['Attrition'] == 'Yes']    

    subset_counts = subset_df[var].value_counts().tolist()
    subset_values = subset_df[var].value_counts().keys().tolist()
    
    total_counts=[]
    
    for x in subset_values:
        total_counts.append((df[var].values == x).sum())
        
    for k,j in enumerate(subset_counts):
        subset_counts[k]= subset_counts[k]/total_counts[k]*100 
        subset_counts[k]=round(subset_counts[k],1)
        
    ax=sns.barplot(x=subset_values, y=subset_counts, color='red', data=subset_df,edgecolor='k' ,alpha=0.8    )
    
    for p in ax.patches:
        txt = str(p.get_height().round(2)) 
        txt_x = p.get_x() 
        txt_y = p.get_height()
        ax.text(txt_x+p.get_width()/ 2.0,txt_y-0.3,txt, ha='center', va='bottom',fontsize=9,color='k')

        rects = ax.patches

    ax.set_ylabel('Percentage of employees in attrition ')
    ax.set_title(var)
    sns.despine(ax=ax, offset=5)  
    
plt.tight_layout()
plt.show()

2.3.9.1 Years at company vs Total working years¶

plt.figure(figsize=(8,4))
sns.scatterplot(x = 'YearsAtCompany', y= 'TotalWorkingYears',hue='Attrition', data = df  ,palette ='flare')
plt.xlabel("YearsAtCompany")
plt.ylabel("TotalWorkingYears")
# plt.legend(loc='lower left', bbox_to_anchor=(0.6,0.5))
plt.legend(shadow=True, fancybox=True, ncol = 5, title="Attrition")
# plt.ylim(top=4000)
plt.show()

2.3.9.2 Years at company vs Year since last promotion¶

plt.figure(figsize=(8,4))
sns.scatterplot(x = 'YearsAtCompany', y= 'YearsSinceLastPromotion',hue='Attrition', data = df  ,palette ='flare')
plt.xlabel("YearsAtCompany")
plt.ylabel("YearsSinceLastPromotion")
# plt.legend(loc='lower left', bbox_to_anchor=(0.6,0.5))
plt.legend(shadow=True, fancybox=True, ncol = 5, title="Attrition")
# plt.ylim(top=4000)
plt.show()

3 . Data Transformation¶

df_transf=df.copy()

df_transf.describe(include=[object]).T

3.1 Transform categorical variables¶

df_transf['OverTime_2'] = df_transf ['OverTime'].replace('No',0).replace('Yes',1)
df_transf['Attrition_2'] = np.where((df['Attrition']=='Yes'),1,0)
df_transf['Single'] = np.where((df['MaritalStatus']=='Single'),1,0)
df_transf['Travel'] = np.where((df['BusinessTravel']=='Non-Travel'),0,1)
# df_transf2=df_transf.drop(['OverTime', 'Attrition','MaritalStatus','BusinessTravel','Department',
#                 'EducationField','Gender','Over18','Attrition_aux','age_bins','HourlyRate_bins'], axis=1)

df_dummies = pd.get_dummies(df_transf)
df_dummies

scaler = MinMaxScaler().fit(df_dummies)
scaled_df = scaler.transform(df_dummies)

cols = df_dummies.columns
scaled_df = pd.DataFrame(scaled_df, columns = cols)
scaled_df.head().T

4. Segmentation¶

4.1 Segmentação attrition='No'¶

df_seg_att=df.loc[df['Attrition'] == 'Yes']

4.1.1 Job Role¶

plt.rcParams['font.size'] = 12
plt.rcParams['figure.dpi'] = 100
variaveis = ['JobRole'] 

Total=df_seg_att['Department'].value_counts().sum()
variaveis_len = len(variaveis)

fig, axes = plt.subplots( nrows=1, ncols=1, figsize=(8,6), dpi=80)
for i,var in enumerate(variaveis):

    subset_counts = df_seg_att[var].value_counts().tolist()
    print(subset_counts)
    subset_values = df_seg_att[var].value_counts().keys().tolist()
    
           
    for k,j in enumerate(subset_counts):
        subset_counts[k]= subset_counts[k]/Total*100 
        subset_counts[k]=round(subset_counts[k],1)
    
    df_new = pd.DataFrame({"subset_counts":subset_counts,
                  "subset_values":subset_values})
        
    ax = sns.barplot(y=subset_values, x=subset_counts, color='green', data=df_new,edgecolor='k',
                     order=df_new.sort_values('subset_counts',ascending = False).subset_values)
    patch_h = []    
    for patch in ax.patches:
        reading = patch.get_height()
        patch_h.append(reading)
   
    idx_tallest = np.argmax(patch_h)   
    # np.argmax return the index of largest value of the list

    ax.patches[idx_tallest].set_facecolor('lightgreen')  
    values=df_new.sort_values('subset_counts',ascending = False).subset_values
    
    for p in ax.patches:
        txt = str(p.get_width().round(2)) 
        
        txt_x = p.get_width() 
        txt_y = p.get_y()
        
        plt.text(txt_x+3.5,txt_y+0.6,txt + " %", ha='center', va='bottom',fontsize='13')

        rects = ax.patches
    
    ax.set_xlabel('Percentage of employees in attrition ')
    ax.set_xlim(0,50)
    ax.set_title(var)
    
    _ = plt.setp(ax.get_xticklabels(), rotation=45) 
    
plt.tight_layout()
plt.show()

[62, 57, 47, 33, 12, 10, 9, 5, 2]

4.1.2 Stock option level¶

plt.rcParams['font.size'] = 14
plt.rcParams['figure.dpi'] = 100
variaveis = ['StockOptionLevel'] 

Total=df_seg_att['Department'].value_counts().sum()
variaveis_len = len(variaveis)

fig, axes = plt.subplots( nrows=1, ncols=1, figsize=(6,4), dpi=80)
for i,var in enumerate(variaveis):
    subset_counts = df_seg_att[var].value_counts().tolist()
    subset_values = df_seg_att[var].value_counts().keys().tolist()
    
    total_counts=[]
      
    for k,j in enumerate(subset_counts):
        subset_counts[k]= subset_counts[k]/Total*100 
        subset_counts[k]=round(subset_counts[k],1)
    
    df_new = pd.DataFrame({"subset_counts":subset_counts,
                  "subset_values":subset_values})
        
    ax = sns.barplot(x=subset_values, y=subset_counts, color='green', data=df_seg_att,edgecolor='k')
    patch_h = []    
    for patch in ax.patches:
        reading = patch.get_height()
        patch_h.append(reading)
   
    idx_tallest = np.argmax(patch_h)   
    # np.argmax return the index of largest value of the list

    ax.patches[idx_tallest].set_facecolor('lightgreen')  
    values=df_new.sort_values('subset_counts',ascending = False).subset_values
    
    for p in ax.patches:
        txt = str(p.get_height().round(2)) 
        txt_x = p.get_x() 
        txt_y = p.get_height()
        ax.text(txt_x+p.get_width()/ 2.0,txt_y-0.3,txt +  ' %', ha='center', va='top',fontsize=16,color='k')
    
    ax.set_ylabel('Percentage of employees in attrition ')
    
    ax.set_title(var)
    
    _ = plt.setp(ax.get_xticklabels(), rotation=0) 
    
plt.tight_layout()
plt.show()

4.1.3 Job level¶

plt.rcParams['font.size'] = 14
plt.rcParams['figure.dpi'] = 100
variaveis = ['JobLevel'] 

Total=df_seg_att['Department'].value_counts().sum()
variaveis_len = len(variaveis)

fig, axes = plt.subplots( nrows=1, ncols=1, figsize=(8,6), dpi=80)
for i,var in enumerate(variaveis):
    subset_counts = df_seg_att[var].value_counts().tolist()
    subset_values = df_seg_att[var].value_counts().keys().tolist()
    
    total_counts=[]
        
    for k,j in enumerate(subset_counts):
        subset_counts[k]= subset_counts[k]/Total*100 
        subset_counts[k]=round(subset_counts[k],1)
    
    df_new = pd.DataFrame({"subset_counts":subset_counts,
                  "subset_values":subset_values})
        
    ax = sns.barplot(x=subset_values, y=subset_counts, color='green', data=df_seg_att,edgecolor='k')
    patch_h = []    
    for patch in ax.patches:
        reading = patch.get_height()
        patch_h.append(reading)
   
    idx_tallest = np.argmax(patch_h)   
    # np.argmax return the index of largest value of the list

    ax.patches[idx_tallest].set_facecolor('lightgreen')  
    values=df_new.sort_values('subset_counts',ascending = False).subset_values
    
    for p in ax.patches:
        txt = str(p.get_height().round(2)) 
        txt_x = p.get_x() 
        txt_y = p.get_height()
        ax.text(txt_x+p.get_width()/ 2.0,txt_y-0.3,txt +  ' %', ha='center', va='bottom',fontsize=16,color='k')
    
    ax.set_ylabel('Percentage of employees in attrition ')
    
    ax.set_title(var)
    
    _ = plt.setp(ax.get_xticklabels(), rotation=0) 
    
plt.tight_layout()
plt.show()

4.1.4 Job involvement¶

plt.rcParams['font.size'] = 14
plt.rcParams['figure.dpi'] = 100
variaveis = ['JobInvolvement'] 


Total=df_seg_att['Department'].value_counts().sum()
variaveis_len = len(variaveis)

fig, axes = plt.subplots( nrows=1, ncols=1, figsize=(8,6), dpi=80)
for i,var in enumerate(variaveis):
    subset_counts = df_seg_att[var].value_counts().tolist()
    subset_values = df_seg_att[var].value_counts().keys().tolist()
    
    total_counts=[]
      
    for k,j in enumerate(subset_counts):
        subset_counts[k]= subset_counts[k]/Total*100 
        subset_counts[k]=round(subset_counts[k],1)
    
    df_new = pd.DataFrame({"subset_counts":subset_counts,
                  "subset_values":subset_values})
        
    ax = sns.barplot(x=subset_values, y=subset_counts, color='green', data=df_seg_att,edgecolor='k')
    patch_h = []    
    for patch in ax.patches:
        reading = patch.get_height()
        patch_h.append(reading)
   
    idx_tallest = np.argmax(patch_h)   
    # np.argmax return the index of largest value of the list

    ax.patches[idx_tallest].set_facecolor('lightgreen')  
    values=df_new.sort_values('subset_counts',ascending = False).subset_values
    
    for p in ax.patches:
        txt = str(p.get_height().round(2)) 
        txt_x = p.get_x() 
        txt_y = p.get_height()
        ax.text(txt_x+p.get_width()/ 2.0,txt_y-0.3,txt +  ' %', ha='center', va='bottom',fontsize=16,color='k')
    
    ax.set_ylabel('Percentage of employees in attrition ')
    
    ax.set_title(var)
    
    _ = plt.setp(ax.get_xticklabels(), rotation=0) 
    
plt.tight_layout()
plt.show()

4.1.5 Business Travel¶

plt.rcParams['font.size'] = 10
plt.rcParams['figure.dpi'] = 100
variaveis = ['BusinessTravel'] 


Total=df_seg_att['Department'].value_counts().sum()
variaveis_len = len(variaveis)

fig, axes = plt.subplots( nrows=1, ncols=1, figsize=(6,3))
for i,var in enumerate(variaveis):

    subset_counts = df_seg_att[var].value_counts().tolist()
    print(subset_counts)
    subset_values = df_seg_att[var].value_counts().keys().tolist()
     
    for k,j in enumerate(subset_counts):
        subset_counts[k]= subset_counts[k]/Total*100 
        subset_counts[k]=round(subset_counts[k],1)
    
    df_new = pd.DataFrame({"subset_counts":subset_counts,
                  "subset_values":subset_values})
        
    ax = sns.barplot(y=subset_values, x=subset_counts, color='green', data=df_new,edgecolor='k',
                     order=df_new.sort_values('subset_counts',ascending = False).subset_values)
    patch_h = []    
    for patch in ax.patches:
        reading = patch.get_height()
        patch_h.append(reading)
   
    idx_tallest = np.argmax(patch_h)   
    # np.argmax return the index of largest value of the list

    ax.patches[idx_tallest].set_facecolor('lightgreen')  
    values=df_new.sort_values('subset_counts',ascending = False).subset_values
    
    for p in ax.patches:
        txt = str(p.get_width().round(2)) 
        
        txt_x = p.get_width() 
        txt_y = p.get_y()
        
        plt.text(txt_x+6,txt_y+0.6,txt + " %", ha='center', va='bottom',fontsize='13')

        rects = ax.patches
    
    ax.set_xlabel('Percentage of employees in attrition ')
    ax.set_xlim(0,80)
    ax.set_title(var)
    
    _ = plt.setp(ax.get_xticklabels(), rotation=45) 
    
plt.tight_layout()
plt.show()

[156, 69, 12]

4.1.6 OverTime¶

plt.rcParams['font.size'] = 10
plt.rcParams['figure.dpi'] = 100
variaveis = ['OverTime'] 


Total=df_seg_att['Department'].value_counts().sum()
variaveis_len = len(variaveis)

fig, axes = plt.subplots( nrows=1, ncols=1, figsize=(6,3))
for i,var in enumerate(variaveis):
    subset_counts = df_seg_att[var].value_counts().tolist()
    print(subset_counts)
    subset_values = df_seg_att[var].value_counts().keys().tolist()
  
    for k,j in enumerate(subset_counts):
        subset_counts[k]= subset_counts[k]/Total*100 
        subset_counts[k]=round(subset_counts[k],1)
    
    df_new = pd.DataFrame({"subset_counts":subset_counts,
                  "subset_values":subset_values})
        
    ax = sns.barplot(y=subset_values, x=subset_counts, color='green', data=df_new,edgecolor='k',
                     order=df_new.sort_values('subset_counts',ascending = False).subset_values)
    patch_h = []    
    for patch in ax.patches:
        reading = patch.get_height()
        patch_h.append(reading)
   
    idx_tallest = np.argmax(patch_h)   
    # np.argmax return the index of largest value of the list

    ax.patches[idx_tallest].set_facecolor('lightgreen')  
    values=df_new.sort_values('subset_counts',ascending = False).subset_values
    
    for p in ax.patches:
        txt = str(p.get_width().round(2)) 
        
        txt_x = p.get_width() 
        txt_y = p.get_y()
        
        plt.text(txt_x+6,txt_y+0.6,txt + " %", ha='center', va='bottom',fontsize='13')

        rects = ax.patches
    
    ax.set_xlabel('Percentage of employees in attrition ')
    ax.set_xlim(0,80)
    ax.set_title(var)
    
    _ = plt.setp(ax.get_xticklabels(), rotation=45) 
    

plt.tight_layout()
plt.show()

[127, 110]

4.1.7 Training times last year¶

plt.rcParams['font.size'] = 9
plt.rcParams['figure.dpi'] = 100
variaveis = ['TrainingTimesLastYear'] 

Total=df_seg_att['Department'].value_counts().sum()
variaveis_len = len(variaveis)

fig, axes = plt.subplots( nrows=1, ncols=1, figsize=(6,4), dpi=100)
for i,var in enumerate(variaveis):
    subset_counts = df_seg_att[var].value_counts().tolist()
    subset_values = df_seg_att[var].value_counts().keys().tolist()
    
    total_counts=[]
    
    for k,j in enumerate(subset_counts):
        subset_counts[k]= subset_counts[k]/Total*100 
        subset_counts[k]=round(subset_counts[k],1)
    
    df_new = pd.DataFrame({"subset_counts":subset_counts,
                  "subset_values":subset_values})
        
    ax = sns.barplot(x=subset_values, y=subset_counts, color='green', data=df_seg_att,edgecolor='k')
    patch_h = []    
    for patch in ax.patches:
        reading = patch.get_height()
        patch_h.append(reading)
   
    idx_tallest = np.argmax(patch_h)   
    # np.argmax return the index of largest value of the list

    ax.patches[idx_tallest].set_facecolor('lightgreen')  
    values=df_new.sort_values('subset_counts',ascending = False).subset_values
    
    for p in ax.patches:
        txt = str(p.get_height().round(2)) 
        txt_x = p.get_x() 
        txt_y = p.get_height()
        ax.text(txt_x+p.get_width()/ 2.0,txt_y-0.3,txt +  ' %', ha='center', va='bottom',fontsize=12,color='k')
    
    ax.set_ylabel('Percentage of employees in attrition ')
    
    ax.set_title(var)
    
    _ = plt.setp(ax.get_xticklabels(), rotation=0) 
    
plt.tight_layout()
plt.show()

4.2 Segmentation by job Role¶

4.2.1 Sales representative (considering attrition)¶

4.2.1.1 Gender¶

var='Gender'
dataframe=df.loc[df['JobRole'] == 'Sales Representative']
plt.rcParams['font.size'] = 10
plt.rcParams['figure.dpi'] = 100

g = sns.catplot(x=var, col="Attrition", col_wrap=4,
                data=dataframe,
                kind="count", height=2.5, aspect=.8, palette='inferno', legend=['yes','no'])
ax = g.facet_axis(0,0)
for p in ax.patches:
    ax.text(p.get_x()+ p.get_width()/2, 
            p.get_height() * 1.02, 
           (p.get_height()),   #Used to format it K representation
            color='black', 
            rotation='horizontal', 
            size='medium',ha='center')
ax = g.facet_axis(0,1)
for p in ax.patches:
    ax.text(p.get_x()+ p.get_width()/2, 
            p.get_height() * 1.02, 
           (p.get_height()),   #Used to format it K representation
            color='black', 
            rotation='horizontal', 
            size='medium',ha='center')
    
subset_df = dataframe[dataframe[var] == 1]    
soma=dataframe.shape[0]
# soma = subset_df[var].value_counts().sum()

ax = g.facet_axis(0,0)
for p in ax.patches:
    ax.text(p.get_x()+ p.get_width()/2, 
            p.get_height() /2.5, "%.1f" % ((p.get_height()/soma)*100) + " %", 
            #Used to format it K representation
            color='w', 
            rotation='horizontal', 
            size='medium', ha='center')    

ax = g.facet_axis(0,1)
for p in ax.patches:
    ax.text(p.get_x()+ p.get_width()/2, 
            p.get_height() /2.5, "%.1f" % ((p.get_height()/soma)*100) + " %", 
            #Used to format it K representation
            color='w', 
            rotation='horizontal', 
            size='medium',ha='center')

plt.show()

4.2.1.2 Overtime¶

var='OverTime'
dataframe=df.loc[df['JobRole'] == 'Sales Representative']
plt.rcParams['font.size'] = 10
plt.rcParams['figure.dpi'] = 100

g = sns.catplot(x=var, col="Attrition", col_wrap=4,
                data=dataframe,
                kind="count", height=2.5, aspect=.8, palette='RdBu', legend=['yes','no'])
ax = g.facet_axis(0,0)
for p in ax.patches:
    ax.text(p.get_x()+ p.get_width()/2, 
            p.get_height() * 1.02, 
           (p.get_height()),   #Used to format it K representation
            color='black', 
            rotation='horizontal', 
            size='medium',ha='center')
ax = g.facet_axis(0,1)
for p in ax.patches:
    ax.text(p.get_x()+ p.get_width()/2, 
            p.get_height() * 1.02, 
           (p.get_height()),   #Used to format it K representation
            color='black', 
            rotation='horizontal', 
            size='medium',ha='center')
    
subset_df = dataframe[dataframe[var] == 1]    
soma=dataframe.shape[0]
# soma = subset_df[var].value_counts().sum()

ax = g.facet_axis(0,0)
for p in ax.patches:
    ax.text(p.get_x()+ p.get_width()/2, 
            p.get_height() /2.5, "%.1f" % ((p.get_height()/soma)*100) + " %", 
            #Used to format it K representation
            color='k', 
            rotation='horizontal', 
            size='medium', ha='center')    
    
ax = g.facet_axis(0,1)
for p in ax.patches:
    ax.text(p.get_x()+ p.get_width()/2, 
            p.get_height() /2.5, "%.1f" % ((p.get_height()/soma)*100) + " %", 
            #Used to format it K representation
            color='k', 
            rotation='horizontal', 
            size='medium',ha='center')
plt.show()

4.2.1.3 Montly Income¶

var='MonthlyIncome' 

collumn=df[var]
plt.rcParams['font.size'] = 12
plt.rcParams['figure.dpi'] = 100
maxim=collumn.max()
minim=collumn.min()
dataframe=df.loc[df['JobRole'] == 'Sales Representative']

lista = dataframe[dataframe['Attrition'] == 'Yes']    

fig, axes = plt.subplots(2,2, figsize = (7,6),gridspec_kw={'height_ratios': [1, 4]})
g1=sns.histplot(ax = axes[1,0], data = lista ,x = var, color="coral", bins = 10)

g2=sns.boxplot(ax = axes[0,0], data = lista, x = var, color="orange" )

g2.set(xlabel=None)
g2.set(title='Attrition = Yes')
res = stats (lista[var])

axes[1,0].text(0, -0.2, 'Mean = ' + str(res[0]), transform=axes[1,0].transAxes, fontsize=10,
       color= 'k')
axes[1,0].text(0.5, -0.2, 'Skewness = ' + str(res[3]), transform=axes[1,0].transAxes, fontsize=10,
       color= 'k')
axes[1,0].text(0, -0.25, 'Q1 = ' + str(res[1]), transform=axes[1,0].transAxes, fontsize=10,
       color= 'k')
axes[1,0].text(0.5, -0.25, 'Q3 = ' + str(res[2]), transform=axes[1,0].transAxes, fontsize=10,
       color= 'k')

plt.xlim(minim,maxim)

plt.xlabel(var)

lista = dataframe[dataframe['Attrition'] == 'No']      

g1=sns.histplot(ax = axes[1,1], data = lista ,x = var, color="dodgerblue", bins = 10)

g2=sns.boxplot(ax = axes[0,1], data = lista, x = var, color="skyblue" )

g2.set(xlabel=None)
g2.set(title='Attrition = No')
res = stats (lista[var])

axes[1,1].text(0, -0.2, 'Mean = ' + str(res[0]), transform=axes[1,1].transAxes, fontsize=10,
       color= 'k')
axes[1,1].text(0.5, -0.2, 'Skewness = ' + str(res[3]), transform=axes[1,1].transAxes, fontsize=10,
       color= 'k')
axes[1,1].text(0, -0.25, 'Q1 = ' + str(res[1]), transform=axes[1,1].transAxes, fontsize=10,
       color= 'k')
axes[1,1].text(0.5, -0.25, 'Q3 = ' + str(res[2]), transform=axes[1,1].transAxes, fontsize=10,
       color= 'k')

axes[1,1].set_ylabel('')  

axes[0,0].set_xlim(500,7000)  
axes[0,1].set_xlim(500,7000)  
axes[1,0].set_xlim(500,7000)  
axes[1,1].set_xlim(500,7000)  

plt.xlabel(var)

plt.show()

4.2.1.4 Job Level¶

var='JobLevel'
dataframe=df.loc[df['JobRole'] == 'Sales Representative']
plt.rcParams['font.size'] = 10
plt.rcParams['figure.dpi'] = 100

g = sns.catplot(x=var, col="Attrition", col_wrap=4,
                data=dataframe,
                kind="count", height=2.5, aspect=.8, palette='inferno', legend=['yes','no'])
ax = g.facet_axis(0,0)
for p in ax.patches:
    ax.text(p.get_x()+ p.get_width()/2, 
            p.get_height() * 1.02, 
           (p.get_height()),   #Used to format it K representation
            color='black', 
            rotation='horizontal', 
            size='medium',ha='center')
ax = g.facet_axis(0,1)
for p in ax.patches:
    ax.text(p.get_x()+ p.get_width()/2, 
            p.get_height() * 1.02, 
           (p.get_height()),   #Used to format it K representation
            color='black', 
            rotation='horizontal', 
            size='medium',ha='center')
    
subset_df = dataframe[dataframe[var] == 1]    
soma=dataframe.shape[0]
# soma = subset_df[var].value_counts().sum()

plt.show()

4.2.1.5 Stock option level¶

var='StockOptionLevel'
dataframe=df.loc[df['JobRole'] == 'Sales Representative']
plt.rcParams['font.size'] = 10
plt.rcParams['figure.dpi'] = 100

g = sns.catplot(x=var, col="Attrition", col_wrap=4,
                data=dataframe,
                kind="count", height=2.5, aspect=.8, palette='inferno', legend=['yes','no'])
ax = g.facet_axis(0,0)
for p in ax.patches:
    ax.text(p.get_x()+ p.get_width()/2, 
            p.get_height() * 1.02, 
           (p.get_height()),   #Used to format it K representation
            color='black', 
            rotation='horizontal', 
            size='medium',ha='center')
ax = g.facet_axis(0,1)
for p in ax.patches:
    ax.text(p.get_x()+ p.get_width()/2, 
            p.get_height() * 1.02, 
           (p.get_height()),   #Used to format it K representation
            color='black', 
            rotation='horizontal', 
            size='medium',ha='center')
    
subset_df = dataframe[dataframe[var] == 1]    
soma=dataframe.shape[0]
# soma = subset_df[var].value_counts().sum()

plt.show()

4.2.1.6 Satisfaction¶

variaveis = ['EnvironmentSatisfaction', 'JobSatisfaction', 'RelationshipSatisfaction','JobInvolvement','WorkLifeBalance'] 
var='JobSatisfaction'
dataframe=df.loc[df['JobRole'] == 'Sales Representative']
plt.rcParams['font.size'] = 10
plt.rcParams['figure.dpi'] = 100

g = sns.catplot(x=var, col="Attrition", col_wrap=4,
                data=dataframe,
                kind="count", height=2.5, aspect=.8, palette='inferno', legend=['yes','no'])
ax = g.facet_axis(0,0)
for p in ax.patches:
    ax.text(p.get_x()+ p.get_width()/2, 
            p.get_height() * 1.02, 
           (p.get_height()),   #Used to format it K representation
            color='black', 
            rotation='horizontal', 
            size='medium',ha='center')
ax = g.facet_axis(0,1)
for p in ax.patches:
    ax.text(p.get_x()+ p.get_width()/2, 
            p.get_height() * 1.02, 
           (p.get_height()),   #Used to format it K representation
            color='black', 
            rotation='horizontal', 
            size='medium',ha='center')
    
subset_df = dataframe[dataframe[var] == 1]    
soma=dataframe.shape[0]
# soma = subset_df[var].value_counts().sum()

plt.show()

4.2.1.7 Business Travel¶

var='BusinessTravel'
dataframe=df.loc[df['JobRole'] == 'Sales Representative']
plt.rcParams['font.size'] = 10
plt.rcParams['figure.dpi'] = 100
plt.rcParams['figure.figsize'] = 8,6

g = sns.catplot(y=var, col="Attrition", col_wrap=4,
                data=dataframe,
                kind="count", height=2.5, aspect=.8, palette='RdBu',edgecolor='k')
ax = g.facet_axis(0,0)
    
for p in ax.patches:
        txt = str(p.get_width()) 
        txt = txt.strip(".0")
        
        txt_x = p.get_width() 
        txt_y = p.get_y()
        
        plt.text(txt_x+3.5,txt_y+0.6,txt, ha='center', va='bottom',fontsize='13')

        rects = ax.patches
ax = g.facet_axis(0,1)
for p in ax.patches:
        txt = str(p.get_width())  
        
        txt_x = p.get_width() 
        txt_y = p.get_y()
        
        plt.text(txt_x+3.5,txt_y+0.6,txt, ha='center', va='bottom',fontsize='13')

        rects = ax.patches
    
subset_df = dataframe[dataframe[var] == 1]    
soma=dataframe.shape[0]
# soma = subset_df[var].value_counts().sum()

plt.show()

posx and posy should be finite values
posx and posy should be finite values

4.2.1.8 Years in current Role¶

var='YearsInCurrentRole' 

collumn=df[var]

plt.rcParams['font.size'] = 10
plt.rcParams['figure.dpi'] = 100

maxim=collumn.max()

minim=collumn.min()

dataframe=df.loc[df['JobRole'] == 'Sales Representative']

lista = dataframe[dataframe['Attrition'] == 'Yes']    

fig, axes = plt.subplots(2,2, figsize = (5,6),gridspec_kw={'height_ratios': [1, 4]})
g1=sns.histplot(ax = axes[1,0], data = lista ,x = var, color="coral", bins = 10)

g2=sns.boxplot(ax = axes[0,0], data = lista, x = var, color="orange" )

g2.set(xlabel=None)
g2.set(title='Attrition = Yes')
res = stats (lista[var])

axes[1,0].text(0, -0.2, 'Mean = ' + str(res[0]), transform=axes[1,0].transAxes, fontsize=10,
       color= 'k')
axes[1,0].text(0.5, -0.2, 'Skewness = ' + str(res[3]), transform=axes[1,0].transAxes, fontsize=10,
       color= 'k')
axes[1,0].text(0, -0.25, 'Q1 = ' + str(res[1]), transform=axes[1,0].transAxes, fontsize=10,
       color= 'k')
axes[1,0].text(0.5, -0.25, 'Q3 = ' + str(res[2]), transform=axes[1,0].transAxes, fontsize=10,
       color= 'k')

plt.xlim(minim,maxim)

plt.xlabel(var)

lista = dataframe[dataframe['Attrition'] == 'No']      

g1=sns.histplot(ax = axes[1,1], data = lista ,x = var, color="dodgerblue", bins = 10)

g2=sns.boxplot(ax = axes[0,1], data = lista, x = var, color="skyblue" )

g2.set(xlabel=None)
g2.set(title='Attrition = No')
res = stats (lista[var])

axes[1,1].text(0, -0.2, 'Mean = ' + str(res[0]), transform=axes[1,1].transAxes, fontsize=10,
       color= 'k')
axes[1,1].text(0.5, -0.2, 'Skewness = ' + str(res[3]), transform=axes[1,1].transAxes, fontsize=10,
       color= 'k')
axes[1,1].text(0, -0.25, 'Q1 = ' + str(res[1]), transform=axes[1,1].transAxes, fontsize=10,
       color= 'k')
axes[1,1].text(0.5, -0.25, 'Q3 = ' + str(res[2]), transform=axes[1,1].transAxes, fontsize=10,
       color= 'k')
axes[1,1].set_ylabel('')  
axes[0,0].set_xlim(minim-1,maxim)  
axes[0,1].set_xlim(minim-1,maxim)  
axes[1,0].set_xlim(minim-1,maxim)  
axes[1,1].set_xlim(minim-1,maxim)  

plt.xlabel(var)

plt.show()

4.2.2 Laboratory Technician (considering attrition)¶

4.2.2.1 Montly Income¶

var='MonthlyIncome' 

dataframe=df.loc[df['JobRole'] == 'Laboratory Technician']

collumn=dataframe[var]
plt.rcParams['font.size'] = 12
plt.rcParams['figure.dpi'] = 100
maxim=collumn.max()
minim=collumn.min()
lista = dataframe[dataframe['Attrition'] == 'Yes']    

fig, axes = plt.subplots(2,2, figsize = (7,6),gridspec_kw={'height_ratios': [1, 4]})
g1=sns.histplot(ax = axes[1,0], data = lista ,x = var, color="coral", bins = 10)

g2=sns.boxplot(ax = axes[0,0], data = lista, x = var, color="orange" )

g2.set(xlabel=None)
g2.set(title='Attrition = Yes')
res = stats (lista[var])

axes[1,0].text(0, -0.2, 'Mean = ' + str(res[0]), transform=axes[1,0].transAxes, fontsize=10,
       color= 'k')
axes[1,0].text(0.5, -0.2, 'Skewness = ' + str(res[3]), transform=axes[1,0].transAxes, fontsize=10,
       color= 'k')
axes[1,0].text(0, -0.25, 'Q1 = ' + str(res[1]), transform=axes[1,0].transAxes, fontsize=10,
       color= 'k')
axes[1,0].text(0.5, -0.25, 'Q3 = ' + str(res[2]), transform=axes[1,0].transAxes, fontsize=10,
       color= 'k')

plt.xlim(minim,maxim)

plt.xlabel(var)

lista = dataframe[dataframe['Attrition'] == 'No']      

g1=sns.histplot(ax = axes[1,1], data = lista ,x = var, color="dodgerblue", bins = 10)

g2=sns.boxplot(ax = axes[0,1], data = lista, x = var, color="skyblue" )

g2.set(xlabel=None)
g2.set(title='Attrition = No')
res = stats (lista[var])

axes[1,1].text(0, -0.2, 'Mean = ' + str(res[0]), transform=axes[1,1].transAxes, fontsize=10,
       color= 'k')
axes[1,1].text(0.5, -0.2, 'Skewness = ' + str(res[3]), transform=axes[1,1].transAxes, fontsize=10,
       color= 'k')
axes[1,1].text(0, -0.25, 'Q1 = ' + str(res[1]), transform=axes[1,1].transAxes, fontsize=10,
       color= 'k')
axes[1,1].text(0.5, -0.25, 'Q3 = ' + str(res[2]), transform=axes[1,1].transAxes, fontsize=10,
       color= 'k')

axes[1,1].set_ylabel('')  

axes[0,0].set_xlim(500,maxim+500)  
axes[0,1].set_xlim(500,maxim+500)  
axes[1,0].set_xlim(500,maxim+500)  
axes[1,1].set_xlim(500,maxim+500)  

plt.xlabel(var)


plt.show()

4.2.2.2 Years in current role¶

var='YearsInCurrentRole' 

dataframe=df.loc[df['JobRole'] == 'Laboratory Technician']

collumn=dataframe[var]

plt.rcParams['font.size'] = 12
plt.rcParams['figure.dpi'] = 100

maxim=collumn.max()

minim=collumn.min()

lista = dataframe[dataframe['Attrition'] == 'Yes']    

fig, axes = plt.subplots(2,2, figsize = (7,6),gridspec_kw={'height_ratios': [1, 4]})
g1=sns.histplot(ax = axes[1,0], data = lista ,x = var, color="coral", bins = 10)

g2=sns.boxplot(ax = axes[0,0], data = lista, x = var, color="orange" )

g2.set(xlabel=None)
g2.set(title='Attrition = Yes')
res = stats (lista[var])

axes[1,0].text(0, -0.2, 'Mean = ' + str(res[0]), transform=axes[1,0].transAxes, fontsize=10,
       color= 'k')
axes[1,0].text(0.5, -0.2, 'Skewness = ' + str(res[3]), transform=axes[1,0].transAxes, fontsize=10,
       color= 'k')
axes[1,0].text(0, -0.25, 'Q1 = ' + str(res[1]), transform=axes[1,0].transAxes, fontsize=10,
       color= 'k')
axes[1,0].text(0.5, -0.25, 'Q3 = ' + str(res[2]), transform=axes[1,0].transAxes, fontsize=10,
       color= 'k')

plt.xlim(minim,maxim)

plt.xlabel(var)

lista = dataframe[dataframe['Attrition'] == 'No']      

g1=sns.histplot(ax = axes[1,1], data = lista ,x = var, color="dodgerblue", bins = 10)

g2=sns.boxplot(ax = axes[0,1], data = lista, x = var, color="skyblue" )

g2.set(xlabel=None)
g2.set(title='Attrition = No')
res = stats (lista[var])

axes[1,1].text(0, -0.2, 'Mean = ' + str(res[0]), transform=axes[1,1].transAxes, fontsize=10,
       color= 'k')
axes[1,1].text(0.5, -0.2, 'Skewness = ' + str(res[3]), transform=axes[1,1].transAxes, fontsize=10,
       color= 'k')
axes[1,1].text(0, -0.25, 'Q1 = ' + str(res[1]), transform=axes[1,1].transAxes, fontsize=10,
       color= 'k')
axes[1,1].text(0.5, -0.25, 'Q3 = ' + str(res[2]), transform=axes[1,1].transAxes, fontsize=10,
       color= 'k')

axes[1,1].set_ylabel('')  

axes[0,0].set_xlim(minim-1,maxim+2)  
axes[0,1].set_xlim(minim-1,maxim+2)  
axes[1,0].set_xlim(minim-1,maxim+2)  
axes[1,1].set_xlim(minim-1,maxim+2)  

plt.xlabel(var)


plt.show()

4.2.2.3 Years at company¶

var='YearsAtCompany' 

dataframe=df.loc[df['JobRole'] == 'Laboratory Technician']

collumn=dataframe[var]

plt.rcParams['font.size'] = 12
plt.rcParams['figure.dpi'] = 100

maxim=collumn.max()

minim=collumn.min()

lista = dataframe[dataframe['Attrition'] == 'Yes']    

fig, axes = plt.subplots(2,2, figsize = (7,6),gridspec_kw={'height_ratios': [1, 4]})
g1=sns.histplot(ax = axes[1,0], data = lista ,x = var, color="coral", bins = 10)

g2=sns.boxplot(ax = axes[0,0], data = lista, x = var, color="orange" )

g2.set(xlabel=None)
g2.set(title='Attrition = Yes')
res = stats (lista[var])

axes[1,0].text(0, -0.2, 'Mean = ' + str(res[0]), transform=axes[1,0].transAxes, fontsize=10,
       color= 'k')
axes[1,0].text(0.5, -0.2, 'Skewness = ' + str(res[3]), transform=axes[1,0].transAxes, fontsize=10,
       color= 'k')
axes[1,0].text(0, -0.25, 'Q1 = ' + str(res[1]), transform=axes[1,0].transAxes, fontsize=10,
       color= 'k')
axes[1,0].text(0.5, -0.25, 'Q3 = ' + str(res[2]), transform=axes[1,0].transAxes, fontsize=10,
       color= 'k')

plt.xlim(minim,maxim)

plt.xlabel(var)

lista = dataframe[dataframe['Attrition'] == 'No']      

g1=sns.histplot(ax = axes[1,1], data = lista ,x = var, color="dodgerblue", bins = 10)

g2=sns.boxplot(ax = axes[0,1], data = lista, x = var, color="skyblue" )

g2.set(xlabel=None)
g2.set(title='Attrition = No')
res = stats (lista[var])

axes[1,1].text(0, -0.2, 'Mean = ' + str(res[0]), transform=axes[1,1].transAxes, fontsize=10,
       color= 'k')
axes[1,1].text(0.5, -0.2, 'Skewness = ' + str(res[3]), transform=axes[1,1].transAxes, fontsize=10,
       color= 'k')
axes[1,1].text(0, -0.25, 'Q1 = ' + str(res[1]), transform=axes[1,1].transAxes, fontsize=10,
       color= 'k')
axes[1,1].text(0.5, -0.25, 'Q3 = ' + str(res[2]), transform=axes[1,1].transAxes, fontsize=10,
       color= 'k')

axes[1,1].set_ylabel('')  

axes[0,0].set_xlim(minim-1,maxim+2)  
axes[0,1].set_xlim(minim-1,maxim+2)  
axes[1,0].set_xlim(minim-1,maxim+2)  
axes[1,1].set_xlim(minim-1,maxim+2)  

plt.xlabel(var)


plt.show()

4.2.2.4 Stock option level¶

var='StockOptionLevel'
dataframe=df.loc[df['JobRole'] == 'Laboratory Technician']
plt.rcParams['font.size'] = 10
plt.rcParams['figure.dpi'] = 100

g = sns.catplot(x=var, col="Attrition", col_wrap=4,
                data=dataframe,
                kind="count", height=2.5, aspect=.8, palette='inferno', legend=['yes','no'])
ax = g.facet_axis(0,0)
for p in ax.patches:
    ax.text(p.get_x()+ p.get_width()/2, 
            p.get_height() * 1.02, 
           (p.get_height()),   #Used to format it K representation
            color='black', 
            rotation='horizontal', 
            size='medium',ha='center')
ax = g.facet_axis(0,1)
for p in ax.patches:
    ax.text(p.get_x()+ p.get_width()/2, 
            p.get_height() * 1.02, 
           (p.get_height()),   #Used to format it K representation
            color='black', 
            rotation='horizontal', 
            size='medium',ha='center')
    
subset_df = dataframe[dataframe[var] == 1]    
soma=dataframe.shape[0]
# soma = subset_df[var].value_counts().sum()


plt.show()

4.2.2.5 Job Level¶

var='JobLevel'
dataframe=df.loc[df['JobRole'] == 'Laboratory Technician']
plt.rcParams['font.size'] = 10
plt.rcParams['figure.dpi'] = 100

g = sns.catplot(x=var, col="Attrition", col_wrap=4,
                data=dataframe,
                kind="count", height=2.5, aspect=.8, palette='inferno', legend=['yes','no'])
ax = g.facet_axis(0,0)
for p in ax.patches:
    ax.text(p.get_x()+ p.get_width()/2, 
            p.get_height() * 1.02, 
           (p.get_height()),   #Used to format it K representation
            color='black', 
            rotation='horizontal', 
            size='medium',ha='center')
ax = g.facet_axis(0,1)
for p in ax.patches:
    ax.text(p.get_x()+ p.get_width()/2, 
            p.get_height() * 1.02, 
           (p.get_height()),   #Used to format it K representation
            color='black', 
            rotation='horizontal', 
            size='medium',ha='center')
    
subset_df = dataframe[dataframe[var] == 1]    
soma=dataframe.shape[0]
# soma = subset_df[var].value_counts().sum()

plt.show()

4.2.2.6 Over Time¶

var='OverTime'
dataframe=df.loc[df['JobRole'] == 'Laboratory Technician']
plt.rcParams['font.size'] = 10
plt.rcParams['figure.dpi'] = 100

g = sns.catplot(x=var, col="Attrition", col_wrap=4,
                data=dataframe,
                kind="count", height=2.5, aspect=.8, palette='RdBu', legend=['yes','no'])
ax = g.facet_axis(0,0)
for p in ax.patches:
    ax.text(p.get_x()+ p.get_width()/2, 
            p.get_height() * 1.02, 
           (p.get_height()),   #Used to format it K representation
            color='black', 
            rotation='horizontal', 
            size='medium',ha='center')
ax = g.facet_axis(0,1)
for p in ax.patches:
    ax.text(p.get_x()+ p.get_width()/2, 
            p.get_height() * 1.02, 
           (p.get_height()),   #Used to format it K representation
            color='black', 
            rotation='horizontal', 
            size='medium',ha='center')
    
subset_df = dataframe[dataframe[var] == 1]   

soma=dataframe.shape[0]
# soma = subset_df[var].value_counts().sum()

plt.show()

4.2.2.7 Work Life balance¶

var='WorkLifeBalance'
dataframe=df.loc[df['JobRole'] == 'Laboratory Technician']
plt.rcParams['font.size'] = 10
plt.rcParams['figure.dpi'] = 100

g = sns.catplot(x=var, col="Attrition", col_wrap=4,
                data=dataframe,
                kind="count", height=2.5, aspect=.8, palette='RdBu', legend=['yes','no'])
ax = g.facet_axis(0,0)
for p in ax.patches:
    ax.text(p.get_x()+ p.get_width()/2, 
            p.get_height() * 1.02, 
           (p.get_height()),   #Used to format it K representation
            color='black', 
            rotation='horizontal', 
            size='medium',ha='center')
ax = g.facet_axis(0,1)
for p in ax.patches:
    ax.text(p.get_x()+ p.get_width()/2, 
            p.get_height() * 1.02, 
           (p.get_height()),   #Used to format it K representation
            color='black', 
            rotation='horizontal', 
            size='medium',ha='center')
    
subset_df = dataframe[dataframe[var] == 1]    
soma=dataframe.shape[0]
# soma = subset_df[var].value_counts().sum()

plt.show()

4.2.2.8 Satisfaction¶

variaveis = ['EnvironmentSatisfaction', 'JobSatisfaction', 'RelationshipSatisfaction','JobInvolvement','WorkLifeBalance'] 
var='JobInvolvement'
dataframe=df.loc[df['JobRole'] == 'Sales Representative']
plt.rcParams['font.size'] = 10
plt.rcParams['figure.dpi'] = 100

g = sns.catplot(x=var, col="Attrition", col_wrap=4,
                data=dataframe,
                kind="count", height=2.5, aspect=.8, palette='RdBu', legend=['yes','no'])
ax = g.facet_axis(0,0)
for p in ax.patches:
    ax.text(p.get_x()+ p.get_width()/2, 
            p.get_height() * 1.02, 
           (p.get_height()),   #Used to format it K representation
            color='black', 
            rotation='horizontal', 
            size='medium',ha='center')
ax = g.facet_axis(0,1)
for p in ax.patches:
    ax.text(p.get_x()+ p.get_width()/2, 
            p.get_height() * 1.02, 
           (p.get_height()),   #Used to format it K representation
            color='black', 
            rotation='horizontal', 
            size='medium',ha='center')
    
subset_df = dataframe[dataframe[var] == 1]    
soma=dataframe.shape[0]
# soma = subset_df[var].value_counts().sum()

plt.show()

4.2.3 Human resources (considering attrition)¶

4.2.3.1 Montly Income¶

var='MonthlyIncome' 

dataframe=df.loc[df['JobRole'] == 'Human Resources']

collumn=dataframe[var]
plt.rcParams['font.size'] = 12
plt.rcParams['figure.dpi'] = 100
maxim=collumn.max()
minim=collumn.min()

lista = dataframe[dataframe['Attrition'] == 'Yes']    

fig, axes = plt.subplots(2,2, figsize = (7,6),gridspec_kw={'height_ratios': [1, 4]})
g1=sns.histplot(ax = axes[1,0], data = lista ,x = var, color="coral", bins = 10)

g2=sns.boxplot(ax = axes[0,0], data = lista, x = var, color="orange" )

g2.set(xlabel=None)
g2.set(title='Attrition = Yes')
res = stats (lista[var])

axes[1,0].text(0, -0.2, 'Mean = ' + str(res[0]), transform=axes[1,0].transAxes, fontsize=10,
       color= 'k')
axes[1,0].text(0.5, -0.2, 'Skewness = ' + str(res[3]), transform=axes[1,0].transAxes, fontsize=10,
       color= 'k')
axes[1,0].text(0, -0.25, 'Q1 = ' + str(res[1]), transform=axes[1,0].transAxes, fontsize=10,
       color= 'k')
axes[1,0].text(0.5, -0.25, 'Q3 = ' + str(res[2]), transform=axes[1,0].transAxes, fontsize=10,
       color= 'k')

plt.xlim(minim,maxim)

plt.xlabel(var)

lista = dataframe[dataframe['Attrition'] == 'No']      

g1=sns.histplot(ax = axes[1,1], data = lista ,x = var, color="dodgerblue", bins = 10)

g2=sns.boxplot(ax = axes[0,1], data = lista, x = var, color="skyblue" )

g2.set(xlabel=None)
g2.set(title='Attrition = No')
res = stats (lista[var])

axes[1,1].text(0, -0.2, 'Mean = ' + str(res[0]), transform=axes[1,1].transAxes, fontsize=10,
       color= 'k')
axes[1,1].text(0.5, -0.2, 'Skewness = ' + str(res[3]), transform=axes[1,1].transAxes, fontsize=10,
       color= 'k')
axes[1,1].text(0, -0.25, 'Q1 = ' + str(res[1]), transform=axes[1,1].transAxes, fontsize=10,
       color= 'k')
axes[1,1].text(0.5, -0.25, 'Q3 = ' + str(res[2]), transform=axes[1,1].transAxes, fontsize=10,
       color= 'k')

axes[1,1].set_ylabel('')  

axes[0,0].set_xlim(500,maxim+500)  
axes[0,1].set_xlim(500,maxim+500)  
axes[1,0].set_xlim(500,maxim+500)  
axes[1,1].set_xlim(500,maxim+500)  

plt.xlabel(var)


plt.show()

4.2.3.2 Over time¶

var='OverTime'
dataframe=df.loc[df['JobRole'] == 'Human Resources']
plt.rcParams['font.size'] = 10
plt.rcParams['figure.dpi'] = 100

g = sns.catplot(x=var, col="Attrition", col_wrap=4,
                data=dataframe,
                kind="count", height=2.5, aspect=.8, palette='RdBu', legend=['yes','no'])
ax = g.facet_axis(0,0)
for p in ax.patches:
    ax.text(p.get_x()+ p.get_width()/2, 
            p.get_height() * 1.02, 
           (p.get_height()),   #Used to format it K representation
            color='black', 
            rotation='horizontal', 
            size='medium',ha='center')
ax = g.facet_axis(0,1)
for p in ax.patches:
    ax.text(p.get_x()+ p.get_width()/2, 
            p.get_height() * 1.02, 
           (p.get_height()),   #Used to format it K representation
            color='black', 
            rotation='horizontal', 
            size='medium',ha='center')
    
subset_df = dataframe[dataframe[var] == 1]   

soma=dataframe.shape[0]
# soma = subset_df[var].value_counts().sum()

plt.show()

4.2.3.3 Training times last year¶

var='TrainingTimesLastYear' 

dataframe=df.loc[df['JobRole'] == 'Human Resources']

collumn=dataframe[var]
plt.rcParams['font.size'] = 12
plt.rcParams['figure.dpi'] = 100
maxim=collumn.max()
minim=collumn.min()

lista = dataframe[dataframe['Attrition'] == 'Yes']    

fig, axes = plt.subplots(2,2, figsize = (7,6),gridspec_kw={'height_ratios': [1, 4]})
g1=sns.histplot(ax = axes[1,0], data = lista ,x = var, color="coral", bins = 10)

g2=sns.boxplot(ax = axes[0,0], data = lista, x = var, color="orange" )

g2.set(xlabel=None)
g2.set(title='Attrition = Yes')
res = stats (lista[var])

axes[1,0].text(0, -0.2, 'Mean = ' + str(res[0]), transform=axes[1,0].transAxes, fontsize=10,
       color= 'k')
axes[1,0].text(0.5, -0.2, 'Skewness = ' + str(res[3]), transform=axes[1,0].transAxes, fontsize=10,
       color= 'k')
axes[1,0].text(0, -0.25, 'Q1 = ' + str(res[1]), transform=axes[1,0].transAxes, fontsize=10,
       color= 'k')
axes[1,0].text(0.5, -0.25, 'Q3 = ' + str(res[2]), transform=axes[1,0].transAxes, fontsize=10,
       color= 'k')

plt.xlim(minim,maxim)

plt.xlabel(var)

lista = dataframe[dataframe['Attrition'] == 'No']      

g1=sns.histplot(ax = axes[1,1], data = lista ,x = var, color="dodgerblue", bins = 10)

g2=sns.boxplot(ax = axes[0,1], data = lista, x = var, color="skyblue" )

g2.set(xlabel=None)
g2.set(title='Attrition = No')
res = stats (lista[var])

axes[1,1].text(0, -0.2, 'Mean = ' + str(res[0]), transform=axes[1,1].transAxes, fontsize=10,
       color= 'k')
axes[1,1].text(0.5, -0.2, 'Skewness = ' + str(res[3]), transform=axes[1,1].transAxes, fontsize=10,
       color= 'k')
axes[1,1].text(0, -0.25, 'Q1 = ' + str(res[1]), transform=axes[1,1].transAxes, fontsize=10,
       color= 'k')
axes[1,1].text(0.5, -0.25, 'Q3 = ' + str(res[2]), transform=axes[1,1].transAxes, fontsize=10,
       color= 'k')

axes[1,1].set_ylabel('')  

axes[0,0].set_xlim(0,maxim+3)  
axes[0,1].set_xlim(0,maxim+3)  
axes[1,0].set_xlim(0,maxim+3)  
axes[1,1].set_xlim(0,maxim+3)  

plt.xlabel(var)


plt.show()

4.2.3.4 Stock option level¶

var='StockOptionLevel'
dataframe=df.loc[df['JobRole'] == 'Human Resources']
plt.rcParams['font.size'] = 10
plt.rcParams['figure.dpi'] = 100

g = sns.catplot(x=var, col="Attrition", col_wrap=4,
                data=dataframe,
                kind="count", height=2.5, aspect=.8, palette='inferno', legend=['yes','no'])
ax = g.facet_axis(0,0)
for p in ax.patches:
    txt = str(p.get_height()) 
    txt = txt.strip(".0")
    ax.text(p.get_x()+ p.get_width()/2, 
            p.get_height() * 1.02, 
            txt, 
            color='black', 
            rotation='horizontal', 
            size='medium',ha='center')

ax = g.facet_axis(0,1)
for p in ax.patches:
    txt = str(p.get_height()) 
    txt = txt.strip(".0")
    ax.text(p.get_x()+ p.get_width()/2, 
            p.get_height() * 1.02, 
            txt, color='black', 
            rotation='horizontal', 
            size='medium',ha='center')
    
subset_df = dataframe[dataframe[var] == 1]    
soma=dataframe.shape[0]
# soma = subset_df[var].value_counts().sum()

plt.show()

posx and posy should be finite values
posx and posy should be finite values

4.2.3.5 Job Level¶

var='JobLevel'
dataframe=df.loc[df['JobRole'] == 'Human Resources']
plt.rcParams['font.size'] = 10
plt.rcParams['figure.dpi'] = 100

g = sns.catplot(x=var, col="Attrition", col_wrap=4,
                data=dataframe,
                kind="count", height=2.5, aspect=.8, palette='inferno', legend=['yes','no'])
ax = g.facet_axis(0,0)
for p in ax.patches:
    txt = int(p.get_height()) 
    
    ax.text(p.get_x()+ p.get_width()/2, 
            p.get_height() * 1.02, 
            txt, 
            color='black', 
            rotation='horizontal', 
            size='medium',ha='center')
 
ax = g.facet_axis(0,1)
for p in ax.patches:
    txt = (p.get_height()).astype('Int64') 
    
    ax.text(p.get_x()+ p.get_width()/2, 
            p.get_height() * 1.02, 
            txt, color='black', 
            rotation='horizontal', 
            size='medium',ha='center')
    
subset_df = dataframe[dataframe[var] == 1]    
soma=dataframe.shape[0]
# soma = subset_df[var].value_counts().sum()

plt.show()

posx and posy should be finite values
posx and posy should be finite values

4.2.3.6 Distance from home¶

var='DistanceFromHome' 

dataframe=df.loc[df['JobRole'] == 'Human Resources']

collumn=dataframe[var]
plt.rcParams['font.size'] = 12
plt.rcParams['figure.dpi'] = 100
maxim=collumn.max()
minim=collumn.min()
lista = dataframe[dataframe['Attrition'] == 'Yes']    

fig, axes = plt.subplots(2,2, figsize = (7,6),gridspec_kw={'height_ratios': [1, 4]})
g1=sns.histplot(ax = axes[1,0], data = lista ,x = var, color="coral", bins = 10)

g2=sns.boxplot(ax = axes[0,0], data = lista, x = var, color="orange" )

g2.set(xlabel=None)
g2.set(title='Attrition = Yes')
res = stats (lista[var])

axes[1,0].text(0, -0.2, 'Mean = ' + str(res[0]), transform=axes[1,0].transAxes, fontsize=10,
       color= 'k')
axes[1,0].text(0.5, -0.2, 'Skewness = ' + str(res[3]), transform=axes[1,0].transAxes, fontsize=10,
       color= 'k')
axes[1,0].text(0, -0.25, 'Q1 = ' + str(res[1]), transform=axes[1,0].transAxes, fontsize=10,
       color= 'k')
axes[1,0].text(0.5, -0.25, 'Q3 = ' + str(res[2]), transform=axes[1,0].transAxes, fontsize=10,
       color= 'k')

plt.xlim(minim,maxim)

plt.xlabel(var)

lista = dataframe[dataframe['Attrition'] == 'No']      

g1=sns.histplot(ax = axes[1,1], data = lista ,x = var, color="dodgerblue", bins = 10)

g2=sns.boxplot(ax = axes[0,1], data = lista, x = var, color="skyblue" )

g2.set(xlabel=None)
g2.set(title='Attrition = No')
res = stats (lista[var])

axes[1,1].text(0, -0.2, 'Mean = ' + str(res[0]), transform=axes[1,1].transAxes, fontsize=10,
       color= 'k')
axes[1,1].text(0.5, -0.2, 'Skewness = ' + str(res[3]), transform=axes[1,1].transAxes, fontsize=10,
       color= 'k')
axes[1,1].text(0, -0.25, 'Q1 = ' + str(res[1]), transform=axes[1,1].transAxes, fontsize=10,
       color= 'k')
axes[1,1].text(0.5, -0.25, 'Q3 = ' + str(res[2]), transform=axes[1,1].transAxes, fontsize=10,
       color= 'k')

axes[1,1].set_ylabel('')  

axes[0,0].set_xlim(0,maxim+2)  
axes[0,1].set_xlim(0,maxim+2)  
axes[1,0].set_xlim(0,maxim+2)  
axes[1,1].set_xlim(0,maxim+2)  

plt.xlabel(var)

plt.show()

4.2.3.7 Satisfaction¶

variaveis = ['EnvironmentSatisfaction', 'JobSatisfaction', 'RelationshipSatisfaction','JobInvolvement','WorkLifeBalance'] 
var='EnvironmentSatisfaction'
dataframe=df.loc[df['JobRole'] == 'Human Resources']
plt.rcParams['font.size'] = 10
plt.rcParams['figure.dpi'] = 100

g = sns.catplot(x=var, col="Attrition", col_wrap=4,
                data=dataframe,
                kind="count", height=2.5, aspect=.8, palette='RdBu', legend=['yes','no'])
ax = g.facet_axis(0,0)
for p in ax.patches:
    ax.text(p.get_x()+ p.get_width()/2, 
            p.get_height() * 1.02, 
           (p.get_height()),   #Used to format it K representation
            color='black', 
            rotation='horizontal', 
            size='medium',ha='center')
ax = g.facet_axis(0,1)
for p in ax.patches:
    ax.text(p.get_x()+ p.get_width()/2, 
            p.get_height() * 1.02, 
           (p.get_height()).astype('Int64') ,   #Used to format it K representation
            color='black', 
            rotation='horizontal', 
            size='medium',ha='center')
    
subset_df = dataframe[dataframe[var] == 1]    
soma=dataframe.shape[0]
# soma = subset_df[var].value_counts().sum()

plt.show()

4.2.3.8 Percent Salary hike¶

var='PercentSalaryHike' 

dataframe=df.loc[df['JobRole'] == 'Human Resources']

collumn=dataframe[var]
plt.rcParams['font.size'] = 12
plt.rcParams['figure.dpi'] = 100
maxim=collumn.max()
minim=collumn.min()
lista = dataframe[dataframe['Attrition'] == 'Yes']    

fig, axes = plt.subplots(2,2, figsize = (7,6),gridspec_kw={'height_ratios': [1, 4]})
g1=sns.histplot(ax = axes[1,0], data = lista ,x = var, color="coral", bins = 10)

g2=sns.boxplot(ax = axes[0,0], data = lista, x = var, color="orange" )

g2.set(xlabel=None)
g2.set(title='Attrition = Yes')
res = stats (lista[var])

axes[1,0].text(0, -0.2, 'Mean = ' + str(res[0]), transform=axes[1,0].transAxes, fontsize=10,
       color= 'k')
axes[1,0].text(0.5, -0.2, 'Skewness = ' + str(res[3]), transform=axes[1,0].transAxes, fontsize=10,
       color= 'k')
axes[1,0].text(0, -0.25, 'Q1 = ' + str(res[1]), transform=axes[1,0].transAxes, fontsize=10,
       color= 'k')
axes[1,0].text(0.5, -0.25, 'Q3 = ' + str(res[2]), transform=axes[1,0].transAxes, fontsize=10,
       color= 'k')

plt.xlim(minim,maxim)

plt.xlabel(var)

lista = dataframe[dataframe['Attrition'] == 'No']      

g1=sns.histplot(ax = axes[1,1], data = lista ,x = var, color="dodgerblue", bins = 10)

g2=sns.boxplot(ax = axes[0,1], data = lista, x = var, color="skyblue" )

g2.set(xlabel=None)
g2.set(title='Attrition = No')
res = stats (lista[var])

axes[1,1].text(0, -0.2, 'Mean = ' + str(res[0]), transform=axes[1,1].transAxes, fontsize=10,
       color= 'k')
axes[1,1].text(0.5, -0.2, 'Skewness = ' + str(res[3]), transform=axes[1,1].transAxes, fontsize=10,
       color= 'k')
axes[1,1].text(0, -0.25, 'Q1 = ' + str(res[1]), transform=axes[1,1].transAxes, fontsize=10,
       color= 'k')
axes[1,1].text(0.5, -0.25, 'Q3 = ' + str(res[2]), transform=axes[1,1].transAxes, fontsize=10,
       color= 'k')

axes[1,1].set_ylabel('')  

axes[0,0].set_xlim(0,maxim+2)  
axes[0,1].set_xlim(0,maxim+2)  
axes[1,0].set_xlim(0,maxim+2)  
axes[1,1].set_xlim(0,maxim+2)  

plt.xlabel(var)


plt.show()

	count	mean	std	min	25%	50%	75%	max
Age	1470.0	36.923810	9.135373	18.0	30.0	36.0	43.00	60.0
DailyRate	1470.0	802.485714	403.509100	102.0	465.0	802.0	1157.00	1499.0
DistanceFromHome	1470.0	9.192517	8.106864	1.0	2.0	7.0	14.00	29.0
Education	1470.0	2.912925	1.024165	1.0	2.0	3.0	4.00	5.0
EmployeeCount	1470.0	1.000000	0.000000	1.0	1.0	1.0	1.00	1.0
EnvironmentSatisfaction	1470.0	2.721769	1.093082	1.0	2.0	3.0	4.00	4.0
HourlyRate	1470.0	65.891156	20.329428	30.0	48.0	66.0	83.75	100.0
JobInvolvement	1470.0	2.729932	0.711561	1.0	2.0	3.0	3.00	4.0
JobLevel	1470.0	2.063946	1.106940	1.0	1.0	2.0	3.00	5.0
JobSatisfaction	1470.0	2.728571	1.102846	1.0	2.0	3.0	4.00	4.0
MonthlyIncome	1470.0	6502.931293	4707.956783	1009.0	2911.0	4919.0	8379.00	19999.0
MonthlyRate	1470.0	14313.103401	7117.786044	2094.0	8047.0	14235.5	20461.50	26999.0
NumCompaniesWorked	1470.0	2.693197	2.498009	0.0	1.0	2.0	4.00	9.0
PercentSalaryHike	1470.0	15.209524	3.659938	11.0	12.0	14.0	18.00	25.0
PerformanceRating	1470.0	3.153741	0.360824	3.0	3.0	3.0	3.00	4.0
RelationshipSatisfaction	1470.0	2.712245	1.081209	1.0	2.0	3.0	4.00	4.0
StandardHours	1470.0	80.000000	0.000000	80.0	80.0	80.0	80.00	80.0
StockOptionLevel	1470.0	0.793878	0.852077	0.0	0.0	1.0	1.00	3.0
TotalWorkingYears	1470.0	11.279592	7.780782	0.0	6.0	10.0	15.00	40.0
TrainingTimesLastYear	1470.0	2.799320	1.289271	0.0	2.0	3.0	3.00	6.0
WorkLifeBalance	1470.0	2.761224	0.706476	1.0	2.0	3.0	3.00	4.0
YearsAtCompany	1470.0	7.008163	6.126525	0.0	3.0	5.0	9.00	40.0
YearsInCurrentRole	1470.0	4.229252	3.623137	0.0	2.0	3.0	7.00	18.0
YearsSinceLastPromotion	1470.0	2.187755	3.222430	0.0	0.0	1.0	3.00	15.0
YearsWithCurrManager	1470.0	4.123129	3.568136	0.0	2.0	3.0	7.00	17.0

	Age	DailyRate	DistanceFromHome	Education	EnvironmentSatisfaction	HourlyRate	JobInvolvement	JobLevel	JobSatisfaction	MonthlyIncome	...	age_bins_(45, 50]	age_bins_(50, 55]	age_bins_(55, 60]	HourlyRate_bins_(30, 40]	HourlyRate_bins_(40, 50]	HourlyRate_bins_(50, 60]	HourlyRate_bins_(60, 70]	HourlyRate_bins_(70, 80]	HourlyRate_bins_(80, 90]	HourlyRate_bins_(90, 100]
EmployeeNumber
1	41	1102	1	2	2	94	3	2	4	5993	...	0	0	0	0	0	0	0	0	0	1
2	49	279	8	1	3	61	2	2	2	5130	...	1	0	0	0	0	0	1	0	0	0
4	37	1373	2	2	4	92	2	1	3	2090	...	0	0	0	0	0	0	0	0	0	1
5	33	1392	3	4	4	56	3	1	3	2909	...	0	0	0	0	0	1	0	0	0	0
7	27	591	2	1	1	40	3	1	2	3468	...	0	0	0	1	0	0	0	0	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2061	36	884	23	2	3	41	4	2	4	2571	...	0	0	0	0	1	0	0	0	0	0
2062	39	613	6	1	4	42	2	3	1	9991	...	0	0	0	0	1	0	0	0	0	0
2064	27	155	4	3	2	87	4	2	2	6142	...	0	0	0	0	0	0	0	0	1	0
2065	49	1023	2	3	4	63	2	2	2	5390	...	1	0	0	0	0	0	1	0	0	0
2068	34	628	8	3	2	82	4	2	3	4404	...	0	0	0	0	0	0	0	0	1	0

	0	1	2	3	4
Age	0.547619	0.738095	0.452381	0.357143	0.214286
DailyRate	0.715820	0.126700	0.909807	0.923407	0.350036
DistanceFromHome	0.000000	0.250000	0.035714	0.071429	0.035714
Education	0.250000	0.000000	0.250000	0.750000	0.000000
EnvironmentSatisfaction	0.333333	0.666667	1.000000	1.000000	0.000000
...	...	...	...	...	...
HourlyRate_bins_(50, 60]	0.000000	0.000000	0.000000	1.000000	0.000000
HourlyRate_bins_(60, 70]	0.000000	1.000000	0.000000	0.000000	0.000000
HourlyRate_bins_(70, 80]	0.000000	0.000000	0.000000	0.000000	0.000000
HourlyRate_bins_(80, 90]	0.000000	0.000000	0.000000	0.000000	0.000000
HourlyRate_bins_(90, 100]	1.000000	0.000000	1.000000	0.000000	0.000000

	Age	Attrition	BusinessTravel	DailyRate	Department	DistanceFromHome	Education	EducationField	EmployeeCount	EnvironmentSatisfaction	...	RelationshipSatisfaction	StandardHours	StockOptionLevel	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager
EmployeeNumber
1	41	Yes	Travel_Rarely	1102	Sales	1	2	Life Sciences	1	2	...	1	80	0	8	0	1	6	4	0	5
2	49	No	Travel_Frequently	279	Research & Development	8	1	Life Sciences	1	3	...	4	80	1	10	3	3	10	7	1	7
4	37	Yes	Travel_Rarely	1373	Research & Development	2	2	Other	1	4	...	2	80	0	7	3	3	0	0	0	0
5	33	No	Travel_Frequently	1392	Research & Development	3	4	Life Sciences	1	4	...	3	80	0	8	3	3	8	7	3	0
7	27	No	Travel_Rarely	591	Research & Development	2	1	Medical	1	1	...	4	80	1	6	3	3	2	2	2	2

	count	unique	top	freq
Attrition	1470	2	No	1233
BusinessTravel	1470	3	Travel_Rarely	1043
Department	1470	3	Research & Development	961
EducationField	1470	6	Life Sciences	606
Gender	1470	2	Male	882
JobRole	1470	9	Sales Executive	326
MaritalStatus	1470	3	Married	673
Over18	1470	1	Y	1470
OverTime	1470	2	No	1054